Вакансии

Location: Singapore About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The Role Nebius is building next-generation data center infrastructure across the APAC to support high-density, GPU-driven workloads. As a Mechanical Design Engineer, you will support the design, review, and technical development of mechanical systems for data centers across the APAC portfolio. This role is ideal for a Mechanical Engineer who can take broad ownership across data center mechanical design, evaluate technical options, and work closely with electrical, construction, commissioning, and operations teams. The right candidate can operate independently, review third-party designs, and help Nebius build scalable, maintainable, high-density data center environments. Responsibilities Review mechanical design packages for data center projects, including internal concepts and third-party partner designs. Support mechanical design development for high-density data center environments. Evaluate mechanical design options for air-cooled and liquid-cooled data center architectures. Coordinate closely with electrical design, construction, commissioning, and operations stakeholders. Review designs for maintainability, operational resilience, capacity, and alignment with Nebius standards. Help assess technical tradeoffs, alternative solutions, product options, and design risks. Support design reviews tied to power density, airflow, cooling capacity, and operational readiness. Requirements Data center mechanical design experience. Strong understanding of mechanical systems in mission-critical or data center environments. Experience with air-cooled data center design. Ability to evaluate mechanical capacity, flows, and design assumptions. Ability to review design documents, technical proposals, and partner deliverables. Ability to read and interpret contracts, design requirements, and technical standards. Strong communication skills with internal stakeholders, external design partners, and construction teams. Broad engineering ownership mindset; able to manage the full mechanical design scope rather than only a narrow specialty. Nice to Have Experience with liquid-cooled data center technologies. Experience with hyper-scale, neocloud, GPU, or AI infrastructure data centers. Experience at companies such as Lambda, CoreWeave, hyperscalers, colocation providers, or mission-critical engineering firms. Familiarity with high-density rack environments, including 50kW to 200kW per rack. Experience supporting design reviews through commissioning or operational turnover. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Location: Tbilisi, Georgia; Limassol, Cyprus Workplace: hybrid TradingView is the world’s largest financial analysis platform with more than 100M users across 180+ countries. We build tools that help traders and investors make informed decisions — from advanced charting and market data to collaboration and publishing features. Our products are used daily by millions of individuals and trusted by companies like Revolut, Binance, and CME Group. We’re continuing to grow and scale our platform, and we’re looking for people who care about product quality, take ownership of their work, and want to build systems used by a global audience. About the team We are building a Reference Data Management platform and looking for a strong data engineer to help shape it. This is a build role rather than a maintenance one. You will work on top of existing services and gradually turn them into a clean, transparent platform, and your decisions will shape its foundational design. Responsibilities Optimize the current platform and turn existing pipelines into transparent, controlled processes Build new components and data processing pipelines Develop and maintain self-service tools for developers, analysts, stewards, and domain owners, so that connecting new data no longer depends on engineers Implement data quality checks and make them available for teams to use and extend Implement golden record determination mechanisms for reference data Take part in hiring as the team grows What makes you the perfect fit 4 to 6 years of experience as a Data Engineer Strong Python. This is the main language of the platform SQL fluency, including query profiling and optimization Deep hands-on experience with at least one batch orchestration tool such as Airflow, Dagster, Prefect, or Kestra Hands-on experience with dbt Hands-on experience with data quality tools such as great expectations, soda, or similar Experience with OLTP and OLAP databases such as Postgres and ClickHouse Experience with data lake or lakehouse and time travel We care about depth more than the exact tool. If your strongest experience is with something close to the tools above, you will pick up ours quickly. Will be a plus Experience owning a key component of a mature data platform What we offer you Flexible working hours and a hybrid work format Well-equipped offices for focused and collaborative work A global, distributed team of 500+ professionals Learning, mentorship, and long-term career growth Relocation support and private health insurance Performance-based bonuses TradingView Premium access Regular team events and company-wide meetups Join the TradingView team and help us build a product used by millions of traders and investors worldwide. We look forward to hearing from you! TradingView is an equal opportunity employer. We embrace diversity and are dedicated to fostering a diverse and inclusive workplace. Our success is driven by 600+ professionals from 40+ countries who speak nearly 20 languages.

Обязанности: - Разрабатывать и обучать модели машинного обучения, включая генераторы кандидатов - Улучшать модели оценки стоимости автомобилей - Разрабатывать решения для антифрода и модерации контента - Подготавливать и трансформировать данные для обучения и продакшена моделей - Упаковывать модели в сервисы и внедрять их в продакшен Требования: - Опыт оркестрации ML‑пайплайнов с помощью Airflow или Luigi - Умение программировать на Python или C++ - Навыки работы с SQL и NoSQL для подготовки и трансформации данных - Умение разворачивать решения в виде микросервисов с использованием FastAPI, gRPC или Docker - Хорошие теоретические знания в области машинного обучения - Понимание устройства и принципов работы одного из ML‑фреймворков или библиотек, например CatBoost или PyTorch

Location: London, England, United Kingdom About Us Fanatics is a leading global sports platform redefining how fans connect with the teams, players and moments they love. Our businesses — Fanatics Commerce, Fanatics Collectibles, Fanatics Betting & Gaming, Fanatics Events, Fanatics Advertising, Fanatics Markets and Fanatics Studios — maximize the presence and reach for hundreds of partners globally and create a one-stop experience that allows fans to buy, bet, collect, and earn, while engaging with sports in deeper, more immersive ways. This includes licensed fan gear and lifestyle products, sportsbook and iGaming, physical and digital trading cards, live events, premium sports entertainment content, credit card offerings and more. Together, they form a powerful, integrated ecosystem — connected by the Fanatics ONE loyalty program and FanCash, the currency of sport — delivering personalized experiences to over 100 million fans worldwide. Fanatics holds partnerships with over 900 sports properties, from professional leagues and players’ associations to teams, colleges and retail partners; over 7,000 athletes and celebrities; and over 2,000 retail locations, including all Lids stores. Backed by a team of more than 22,000 employees, Fanatics is committed to relentlessly enhancing the fan experience. Data Engineer III About the Role We're looking for a Data Engineer III to join our Data Engineering team, which builds and governs the data foundation that powers the business. You'll work within our stack — Python ingestion pipelines, Airflow orchestration, and Snowflake/Databricks — helping move data reliably and securely from source to decision-ready output. You'll implement features and fixes against a given design, handle known classes of pipeline issues on your own, and escalate genuinely novel problems with clear context rather than working them in isolation. You're also expected to start contributing meaningfully in code review — catching real bugs, not just style nits. What You'll Do Implement new ingestion sources end-to-end against a senior engineer's design — connector code, DAG, schema, monitoring, and catalog registration — extending the team's existing framework where a new source needs a pattern it doesn't yet support Investigate pipeline failures independently, recognize known classes of issues, and ship the documented fix without needing to escalate Before shipping a new pipeline, identify downstream consumers and what would break if data were late or wrong, and flag gaps like this during spec review — before writing code Write clear handovers when escalating a genuinely unresolved issue — what you tried, what you ruled out, and where things diverge — so a senior can pick up without re-discovery Review peers' pipeline PRs and catch non-obvious issues (e.g., missing idempotency checks, race conditions) that could cause incorrect downstream data Turn ambiguous "why is this data wrong?" questions into structured investigations — tracing data lineage from source to warehouse and communicating back what you found Surface concerns in spec review as specific, well-reasoned questions rather than staying silent or blocking progress Support data security and governance work (e.g., PII masking, access controls) and contribute to data delivery work, including reverse ETL integrations Build strong working relationships with internal stakeholders and help scope and clarify requirements for new work Mentor DE2s on their first significant projects — pairing on tricky decisions and helping them apply team conventions What We're Looking For 3–5 years of professional software or data engineering experience Strong SQL and Python skills, with solid experience building and operating production data pipelines Comfort investigating and root-causing pipeline issues independently before escalating Experience with workflow orchestration tools (Airflow or similar) and a cloud data warehouse/lakehouse (Snowflake, Databricks, or similar) Solid understanding of data pipeline concepts: idempotency, schema evolution, backfills, and data quality/testing Experience giving substantive code review feedback, not just style or formatting comments Strong communication skills — can write a clear technical handover, ask sharp questions in spec review, and explain a data lineage investigation to a non-technical stakeholder A track record of taking ownership of known-class problems end-to-end rather than needing step-by-step direction Nice to Have Experience extending or building reusable pipeline frameworks/templates Exposure to reverse ETL tools or patterns, PII masking, or data access governance (RBAC) Exposure to observability/monitoring tooling (e.g., Datadog) for pipeline health and alerting Some experience mentoring or informally supporting more junior engineers Background in gaming, betting, e-commerce, or another regulated/high-compliance industry Why Join Us Real ownership over known-class problems, with senior/staff support available for the genuinely novel ones Work on high-visibility, high-trust systems that the business depends on A culture built around clear tenets: standardize before you scale, own the outcome (not just the ticket), and clarity over complexity Clear growth path into Senior Data Engineer, with room to start mentoring and shaping team practices along the way By submitting your application, you agree to our terms of service and acknowledge you have read our Candidate Privacy Policy. Fanatics will never ask candidates for money, banking information, or other sensitive personal information as part of the interview process. If you receive a suspicious message claiming to be from Fanatics recruiting, please do not respond and report it here .

Location: San Francisco Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW As the Engineering Manager for Model Infrastructure, you'll lead the team responsible for the platform powering every model request across Harvey. You'll partner closely with AI Research, Product Engineering, Infrastructure, and external AI providers to ensure our platform remains reliable, scalable, and cost-efficient as our business grows. Model Infrastructure is one of Harvey's most strategic engineering organizations. Every product capability—from chat experiences and agents to document workflows and future reasoning systems—depends on this platform. Over the next several years, the team will evolve beyond operating third-party models to building the infrastructure that enables Harvey to train, evaluate, deploy, and operate our own frontier AI models. This role offers the opportunity to shape the technical foundation of Harvey's AI platform and build an organization that will power the company's next phase of growth. WHAT YOU'LL DO - Lead and grow a high-performing team of software engineers responsible for Harvey's Model Infrastructure platform. - Define the technical roadmap for model reliability, scalability, and operational excellence. - Build highly reliable systems for model provisioning, capacity management, failover, and incident response across multiple AI providers. - Own Harvey's multi-provider model platform, including provider integrations, SDK upgrades, API migrations, and onboarding new model providers. - Drive the evolution of our Unified Model Controller (UMC) and Model Selector platform to automatically detect degraded models and intelligently route traffic based on health, latency, quality, compliance, and cost. - Improve observability through health dashboards, alerting, token usage analytics, cost reporting, and end-to-end model telemetry. - Partner with Product Engineering to support new model launches, capacity planning, experimentation, and proactive production monitoring. - Lead initiatives to improve inference efficiency, reduce infrastructure costs, and increase model utilization across providers. - Build the infrastructure foundation for Harvey's future model training efforts, including data pipelines, model operations, training environments, and AI platform capabilities. - Partner with executive leadership on long-term AI infrastructure strategy and vendor relationships. - Recruit, mentor, and develop exceptional engineering talent while fostering a culture of technical excellence and operational ownership. WHAT YOU HAVE - 8+ years of software engineering experience, including multiple years managing high-performing engineering teams. - Experience leading teams responsible for large-scale distributed systems or cloud infrastructure. - Strong technical background that enables you to guide architectural decisions and mentor senior engineers. - Experience operating highly available production services with strong reliability and operational excellence. - Experience building platforms that require scalability, observability, automation, and cost optimization. - Strong cross-functional leadership skills with the ability to partner effectively across Engineering, Research, Product, and external vendors. - Excellent communication skills and the ability to influence technical strategy across organizations. - A passion for building teams and developing engineering talent. NICE TO HAVE - Experience with AI infrastructure, LLM serving, or machine learning platforms. - Experience working with multiple model providers such as OpenAI, Anthropic, Azure OpenAI, Fireworks, Baseten, or open-source model ecosystems. - Experience building inference platforms, model gateways, traffic routing systems, or policy-based serving infrastructure. - Experience with Kubernetes, cloud infrastructure, distributed systems, and large-scale observability platforms. - Experience supporting GPU infrastructure, model training platforms, or ML infrastructure. - Familiarity with data platforms and technologies such as Spark, Kafka, Flink, Airflow, or Iceberg. - Experience leading organizations through periods of rapid growth and technical transformation. COMPENSATION $260,000 - $350,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-AN2 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: Remote Canada At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most. On the ML Fraud team, you’ll build and improve machine learning systems that make real-time transaction decisions, protecting consumers and merchants while balancing fraud loss, customer experience, and conversion. You’ll work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring as fraud patterns evolve. What you’ll do - You will lead development of new fraud prediction models using a mix of approaches for tabular, graph, and behavioral data - You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed. - You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls. - You productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness. - You will instrument and monitor model and data health, and help define retraining/backtesting workflows as fraud patterns evolve. - Identify and implement foundational improvements to how the team builds models. - You will collaborate across Engineering, Fraud Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences. What we look for - You have 6+ years experience researching, training, tuning and launching ML models at scale. Relevant PhD can count for up to 2 years of experience. - Track record of delivering high impact machine learning models in a low latency live setting - Strong Python skills and experience writing production-quality code. - Experience building and evaluating models for tabular classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost, or similar). - Experience with a deep learning framework (PyTorch preferred). - Experience working with distributed data processing or parallel compute frameworks (Spark preferred; Ray/Dask or similar). - Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring (e.g., Kubeflow, Airflow, MLflow, or equivalent internal platforms). - Proficient in using AI-powered developer tools (e.g., Claude Code, Cursor, or similar) to accelerate iteration, debugging, and code quality as part of day-to-day development workflows. - You have mastered taking a simple problem or business scenario into a solution that interacts with multiple software components, and executing on it by writing clear, easily understood, well tested and extensible code. - You are comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews. - Your experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders. - You have strong verbal and written communication skills that support effective collaboration with our global engineering team. Pay Grade - N Equity Grade - 6 Employees new to Affirm typically come in at the start of the pay range. Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills. Base pay is part of a total compensation package that may include monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents). In addition, the employees may be eligible for equity rewards offered by Affirm Holdings, Inc. (parent company). CAN base pay range per year: $153,000 - $213,000 Location - Remote Canada This remote role is open only to candidates residing in Alberta, British Columbia, Manitoba, New Brunswick, Newfoundland and Labrador, Nova Scotia, Ontario, Prince Edward Island, or Saskatchewan. #LI-Remote Remote-first with flexibility built in Affirm is proud to be a remote-first company. Most roles can be done from almost anywhere within the country of employment. Some positions may occasionally require in-person work at an Affirm office, and a few are office-based due to the nature of the work. All new hires will be invited to attend an in-person onboarding experience. Benefits designed for you Our benefits reflect our commitment to care, transparency, and flexibility. Here are a few highlights: Health coverage at no cost: We cover 100% of premiums for employees and their dependents. Spending stipends: Monthly stipends support your tech setup, and the ability to choose health and wellness options that are right for you. Time off to recharge: Flexible time off and generous holiday calendars help you rest when you need to. Own a piece of what you build: Our employee stock purchase plan (ESPP) lets you buy Affirm stock at a discount. We’re committed to providing an inclusive interview process, including accommodations for candidates with disabilities. If you need support, we’re happy to help. For positions based in San Francisco or Los Angeles: Affirm considers qualified applicants with arrest and conviction records, as required by law. By clicking "Submit Application," you acknowledge that you have read Affirm's Global Candidate Privacy Notice and consent to the use of your personal information as described.

Задачи: - Автоматизировать тестирование компонентов Arenadata Hyperwave (ADH) и Arenadata Hyperwave Cloud (ADHC): Spark, Impala, Trino, Kyuubi, Hive, HBase, Ozone, HDFS, YARN, Airflow, Flink; - Тестировать развёртывание и эксплуатацию платформы на bare metal, VM и в Kubernetes; - Разрабатывать и развивать тестовые фреймворки на Python для распределённых Big Data-систем; - Писать интеграционные и e2e-тесты на реальных кластерах: функциональность, отказоустойчивость, производительность; - Поддерживать и развивать CI/CD-пайплайны автотестов; - Работать с разработчиками, архитекторами и QA над качеством релизов; - Делать код-ревью тестового кода. Требования: - Опыт коммерческой разработки на Python от 4 лет; - Умение применять SOLID, DRY, KISS при проектировании тестовых фреймворков; - Знание паттернов проектирования (Factory, Strategy, Builder, Resource Object) для гибких и масштабируемых решений; - Экспертное владение pytest: сложные фикстуры, параметризация, плагины (pytest-xdist и другие); - Опыт использования Linux (Core Utils, Bash): файловые системы, права, процессы, сетевые утилиты; - Опыт тестирования систем развёртывания и конфигурации распределённого ПО, плюсом будет Ansible, Terraform или Puppet; - Сетевой бэкграунд: HTTP(S), TCP/IP, SSH, сертификаты; - Опыт настройки и поддержки CI/CD-пайплайнов (Jenkins, GitLab CI, TeamCity, GitHub Actions); - Опыт работы с Docker и Kubernetes на уровне эксплуатации и отладки.

Location: Spain; Serbia; Poland; Ukraine; Portugal; North Macedonia; Bosnia and Herzegovina; Romania Workplace: remote About Provectus Provectus is an AWS Premier Partner and an Anthropic Strategic Partner, working at the frontier of applied AI. We help enterprises turn Claude, agentic systems, and their own data into measurable business outcomes — through bespoke applications, managed services, and advisory engagements. With offices in North America, LATAM, and EMEA, we partner with clients worldwide.   Our work centers on two verticals — Financial Services & Insurance and Healthcare & Life Sciences — where we deploy five pre-built AI Blueprints: Submission Flow, Portfolio Lens, Asset Flow, Revenue Flow, and Evidence Lens. Each Blueprint rebuilds a critical business process front to back, shipped from working code and tuned to a client's specific book, regulators, and operating posture.   Our team holds 100+ AWS certifications, is Claude Code certified, and co-delivers Anthropic's Agentic SDLC program, Cowork Activation, and AI Blueprint engagements. Where this role sits You will work in a small, senior pod alongside an FDE and an FDX, Forward Deployed Executives (FDX) own the commercial relationship and the business outcome. Works alongside the client's leadership or C-suite level to move the client's KPIs. Forward Deployed Engineers (FDE) embed with a client, map the client workflow, identify the business problem underneath it, design and build a working AI solution, present to the client, and transfer the knowledge to the client's team. Owns technical direction of the whole solution. Senior AI Engineer. When an FDE comes back from the client with the business problem, you will help turn that into an agentic system that runs in production and will be responsible for evaluation, observability, and guardrails.  You'll have real ownership of components and of the technical decisions inside them. Requirements: Mindset Proactive and self-directed; you push for clarity rather than waiting for a ticket Excellent communication and problem-solving skills Comfort with ambiguity and ownership. B2+ English, comfortable collaborating across distributed, multicultural teams. Technical depth 5+ years in software or ML engineering, with production systems you were accountable for . Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes. Shipped to production LLM applications and agentic workflows — not demos, not POCs, not notebooks. Agentic orchestration: multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks. Experience building and optimizing RAG systems in production. Strong engineering fundamentals. Full-stack mindset, comfortable across AI, backend development, and cloud infrastructure. Python and/or TypeScript proficiency; depth matters more than stack. Dropped into an unfamiliar codebase, you're productive. Hands-on AWS in production: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar. GCP or Azure is a plus. Cloud-native delivery: containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines. You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured, how you produced ground truth, and what gated a release. Model and agent monitoring, drift detection. Cost and latency discipline: model tiering, caching, and the ability to say what a workload costs to run before it runs. Hands-on production experience with the Claude ecosystem —  Claude Code, CLAUDE.md, hooks, skills files.  Spec-driven development — writing the intent, constraints, and acceptance criteria before you let an agent build — is a strong plus. MCP: you can say why an agent would prefer it to a REST integration. Having authored a server is a plus. Responsibilities: Work in a pair with an FDE and an FDX. Build and ship production GenAI systems into the customer’s environment (cloud-native data, LLM-based, and agentic AI solutions). Build and optimize RAG systems for production use cases Build the evaluation harness before you build the feature. Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer. Integrate AI components into backend services and RESTful APIs Take systems to production on AWS (GCP or Azure where the customer requires it): containerised, CI/CD. Implement LLMOps and AgentOps practices: agent tracing, prompt and version management, cost and latency monitoring, regression testing, drift detection Start from the blueprint, contribute to enablement and handover: clear documentation, runbooks, and pairing with the client engineers who will inherit the system. Feed reusable components and lessons back into the Provectus Blueprints Participate in technical discussions and architectural decisions Conduct model evaluation, improve failure modes you find, optimize model performance, efficiency, and reliability Mentor junior and mid-level AI engineers, conduct code reviews and share knowledge across the team through documentation, presentations, and workshops. Nice to Have: Experience in one of the industries: financial services, insurance, healthcare. Consulting, professional services, or other embedded customer-facing delivery. AWS and Claude Code Certifications A2A: you can explain agent-to-agent interoperability CI/CD pipeline experience (GitHub Actions, GitLab CI) Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines. Experience in an additional language (Go, TypeScript, or Rust). Experience with Apache Spark, Apache Airflow, Kafkа What We Offer: The chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment A forward-deployed model working in small, senior teams alongside FDE and FDX A growing AI delivery practice where you help build the tooling and frameworks, not just use them Remote-friendly culture Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance Career growth; we actively develop our engineers Access to the latest AI tools and premium subscriptions Long-term B2B collaboration Private medical insurance or a budget for your medical needs Paid sick leave, vacation, and public holidays Equipment and all the tech you need for comfortable, productive work How we hire:   Intro conversation. The role, your background and aspirations, tech questions. Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant  HR Interview. Soft skills and expectations HM interview. Tech questions; a live engine

Location: Portugal; Albania; Georgia; Greece; Bulgaria; Kosovo; Bosnia and Herzegovina; Armenia; Italy; Spain; Malta; Romania; Ireland; Croatia; Serbia; Montenegro Workplace: Remote ABOUT BLOCK LABS Block Labs is a premier technology studio operating at the bleeding edge of Web3, Artificial Intelligence, and iGaming. We don't just ship features; we engineer high-scale, production-grade platforms that power the next generation of digital products. We are a collective of senior engineers, product strategists, and builders who refuse to compromise on architecture. Whether we are designing autonomous multi-agent AI systems, building decentralized financial infrastructure, or architecting high-frequency iGaming platforms, our standard is excellence. We move fast, but we build for the long term. If you are looking to work alongside a team that values deep technical expertise, thoughtful system design, and product ownership, Block Labs is where you belong. THE ROLE Data & Intelligence now sits at the centre of several products we are developing, and we need a platform that is both dependable and capable of supporting more advanced intelligence over time. This role reflects that shift. We are designing a new data platform that will act as the backbone for everything from real time decisioning to predictive modelling. As a Data Platform Engineer in the Data Team, you will own the end-to-end real-time pipeline, serving data across a unified analytical warehouse and feature-serving layer. You are not building dashboards. You are engineering the commercial nervous system of a multi-tenant platform designed to scale from one operator to 10x with marginal infrastructure cost. KEY RESPONSIBILITIES: - Design, build, and maintain scalable data pipelines using AWS Glue (PySpark), or equivalent orchestration and transformation tools. - Engineer and optimise the ClickHouse warehouse for sub-second query performance across all back-offices. - Implement data contracts between back-office and the platform. Onboarding a new operator is a config change, not new tables, topics, or feature views. - Build the feature-serving layer providing pre-computed features to AI agents at millisecond latency. - Integrate with third-party databases, back-office APIs, and external systems (CRM, affiliates, acquisition platforms). - Establish monitoring, alerting, and maintenance procedures including pipeline health checks, freshness monitoring, anomaly detection, and data contract SLA enforcement. - Own CI/CD and infrastructure-as-code for data workloads. - Collaborate with data scientists, agent engineers, BI developers, and infrastructure teams to translate data requirements into reliable, production-grade pipelines. ABOUT YOU: - 3+ years building and operating production data pipelines at scale, with hands-on experience across both streaming and batch paradigms. - Expertise in Apache Kafka (or Amazon MSK): topic design, consumer group management, offset handling, schema registry operations, and production troubleshooting of lag, rebalancing, and throughput issues. - Strong SQL and warehouse engineering skills: experience with columnar analytical databases (ClickHouse strongly preferred, or similar: Druid, BigQuery, Redshift). - PySpark / Spark Streaming proficiency: writing transformation jobs that normalise, enrich, and enforce business rules on event streams. Experience with AWS Glue, Apache Airflow, or Apache NiFi is a strong plus. - Data modelling discipline: ability to design normalised, multi-tenant schemas where tenant isolation is a filter, not a fork. Experience with data contracts and schema governance. - CI/CD and infrastructure-as-code experience: automated testing of data pipelines, version-controlled deployments (CloudFormation, Terraform, or CDK), and familiarity with containerised workloads (ECS Fargate or Kubernetes). - Data quality and observability mindset: experience implementing pipeline health monitoring, automated data validation (Great Expectations or equivalent), freshness checks, and anomaly detection. NICE TO HAVE - Experience in iGaming, online casino, poker, or sportsbook platforms. - Exposure to blockchain or crypto-native transaction flows, including on-chain event ingestion, token-denominated accounting, or stablecoin settlement. - Comfortable operating in an AWS-native environment (MSK, Glue, S3, DynamoDB, ECS, IAM). You understand serverless tradeoffs and can size infrastructure for cost efficiency. - Feature store experience (SageMaker, Feast, or Tecton) building offline/online feature pipelines that serve ML models at inference time. - Prior work in regulated industries (financial services, gambling, fintech) where data lineage, auditability, and compliance are non-negotiable. - Experience migrating legacy query engines (Athena, Trino, Presto) to modern analytical warehouses with reconciliation frameworks to validate correctness. HOW WE WORK - Fully remote with asynchronous-first communication. EU time zone overlap is preferred. - Small, high-autonomy team within the Data function. You report to the Head of Data and co-ordinate with the AI, BI, and Infrastructure Teams. - Architecture decisions are documented and debated. You will participate in design reviews and own your domain decisions. - We build for multi-tenant scale from day one. Every pipeline, schema, and contract you ship must absorb a new operator without engineering effort. - On-call rotation will be established in the run phase. During the build phase, the focus is velocity with quality. No firefighting legacy systems. What kind of culture can I expect? Mature, mission-driven, and low-ego. We value clarity over noise, outcomes over theatrics, and pace without chaos. If you’re one of the smartest minds in your craft and want to build with other experts, you’ll feel at home here.

Задачи: - Внедрять AI-инструменты, агентов и сценарных ботов в продукты и сервисы. - Разрабатывать микросервисы для обработки данных. - Проектировать и оптимизировать ETL-процессы. - Поддерживать и развивать потоковые и пакетные системы обработки данных. - Повышать производительность, отказоустойчивость и масштабируемость существующих решений. - Интегрировать различные системы и источники данных. - Автоматизировать мониторинг и систему алертинга. Требования: - Опыт работы с данными и их трансформацией с использованием SQL (ClickHouse, PostgreSQL, Vertica, Trino и другие). - Уверенное знание Python. - Опыт работы с инструментами оркестрации процессов (Airflow). - Понимание архитектуры и принципов построения ETL-пайплайнов

Location: Serbia POWER A WORLD OF TRUST Incode is the leading provider of world-class identity solutions that is reinventing the way humans authenticate and verify their identities online to power a world of digital trust. Through our revolutionary identity solutions, we are unleashing the business potential of universal industries including finance, government, retail, hospitality, gaming, and more, by reducing fraud and transforming human interactions with data, products, and services. We’re in the process of rapidly scaling our diverse global team and we’re looking for entrepreneurial individuals and leaders who are curious, driven, and excited by ownership to join a Unicorn-status scale-up! About Incode Incode is a Series B unicorn rewriting how the world proves identity. Our AI-powered platform lets leading banks, fintechs, marketplaces, and governments deliver friction-free experiences while defeating fraud and safeguarding privacy. Customers such as Citi, AirBnB, Block, Chime, Sixt, and TikTok rely on Incode to power their identity verification and security. Recently named a Leader in the Gartner® Magic Quadrant™ for Identity Verification, we're scaling fast and we're looking for an Analytics Team Lead to make the quality of our identity technology visible, measurable, and relentlessly data-driven. The Impact You'll Make This role sits on the Document Intelligence team — the group behind Incode's industry-leading document processing. Our technology parses identity documents from around the world — passports, visas, driver's licenses, proof of address, and more — for some of the biggest enterprises on the market. Machine learning is the core of the product, and Analytics is what makes its quality visible and its roadmap data-driven. We're looking for a Senior Lead of Data Analytics who is, first and foremost, a sharp hands-on analyst — and who has the leadership experience to grow a small, high-impact team around them. Today the team is three people; you'll be the fourth, owning it end-to-end. This is a true player-coach role: you'll manage and mentor the team while personally writing the pipelines, queries, and analyses. Our analytics is partially built — some data pipelines and testing procedures are already in place, but metric coverage and visibility are far from complete. Your mission is to close that gap. What You'll Own & Drive Lead & Grow the Team — Run the analytics team as a formal manager: own the roadmap, hold 1:1s and performance reviews, develop your analysts, and participate in hiring as the team scales. Stay Hands-On — This role involves a lot of personal execution. You'll write SQL and Python pipelines for data collection, processing, and labeling — and do the analysis yourself. Expand the Metric Tree — Build out the end-to-end metric tree that measures product quality across the document-processing pipeline, closing coverage gaps where the product is currently blind. Build Visibility — Create reporting and dashboards that surface weak points and turn raw signals into clear insights for ML, product, and leadership. Own ML Quality Evaluation — Drive data preparation for ML, model quality assessment, and benchmarking, in tight collaboration with the ML team. Drive Experimentation — Design, run, and interpret A/B tests across mobile and server side, with genuine statistical rigor. Guard Data & Labeling Quality — Monitor accuracy and consistency, and build the controls that keep them high. Bridge to Engineering — When events or data are missing, pinpoint exactly what's needed and where it should come from, write clear and actionable tasks for the backend and data teams, and justify their prioritization in business terms. Systematize — Turn one-off analyses into repeatable, owned pipelines and reporting cadences. Your Background Team leadership experience , paired with a genuine appetite to stay hands-on — you lead and you build. Strong Python , with a bias toward clean, maintainable, well-structured code. Strong SQL and solid experience with columnar / analytical databases. Strong statistics and experimental design — you can design tests (A/B and beyond), reason about significance and power, and interpret results correctly. Modern data tooling — experience with workflow orchestration, data transformation, cloud storage, and experiment-tracking. Our stack is Python, S3, Redshift, ClearML, Airflow, and dbt; equivalent tools are perfectly fine — what matters is that you've worked with this kind of stack. Excellent communication — you can translate a data gap into a prioritized, business-justified requirement, talk to engineers and stakeholders in their own language, and make the case for what gets built. High ownership, autonomy, and pragmatism — comfortable prioritizing impact over perfection in an evolving environment. Bonus points for: Experience with ML-centered products — evaluating model quality and working alongside ML teams. You won't be expected to train production models; at most simple models for data filtering, plus the ability to run existing ones. Familiarity with data labeling processes and quality estimation/maintenance. Domain exposure to identity documents, OCR, or computer-vision pipelines. The Qualities That Set You Apart A player-coach at heart — you draw energy from both growing people and doing the work yourself. Craftsmanship in how you build — clean code, sound pipelines, and analyses others can trust and reuse. Intellectual honesty and statistical rigor — you follow the data where it leads and know when a result is real. A translator's instinct — you turn ambiguous signals into clear insight and make the business case for what to build next. Pragmatism and high ownership — you thrive in an evolving environment and push for impact over perfection. Why Incode? Mission with Meaning — Shape how billions of people prove identity — safely, simply, and ethically. Rocket-Ship Growth — Join at a defining moment where your work compounds in global impact. Elite Team & Technology — Collaborate with top-tier engineers, researchers, and scientists at the forefront of AI. Ownership & Autonomy — Operate with the freedom to test, build, and ship your ideas end-to-end. Global Impact — Your work will shape secure, frictionless identity verification experiences across continents. Aspects of our Culture: High performance Freedom & responsibility Context, not control Highly aligned, loosely coupled Continuous Feedback Promotions & Development Learn more about Life at Incode ! Benefits & Perks: Flexible Working Hours & Workplace Open Vacation Policy Equal Opportunities: Incode is an equal opportunity employer, committed to creating a diverse and inclusive work environment. We take great pride in having an inclusive, diverse, and global team, and we are always looking for talented and passionate individuals from all backgrounds and walks of life. As part of our commitment to inclusion, we ensure that reasonable accommodations are available throughout the hiring process. If you require any accommodation due to a disability or specific need, please let our Talent Acquisition team know—we’ll do our best to support you. Applicant Data Privacy: We will only use your personal information concerning Incode’s application, recruitment, and hiring processes.

Location: Armenia - Remote; Europe; Georgia - Remote; Portugal - Remote; Serbia - Remote; Spain - Remote About Smartcat Smartcat is building the future of work, where human expertise meets digital teammates to drive 10x to 1000x productivity gains for the world’s leading enterprises. We’re on the frontier of an entirely new category: Agentic AI. We enable enterprises to build high-performing hybrid workforces made up of both humans and AI agents. These AI agents aren’t generic copilots. They’re fully trained digital teammates that learn from your best people, your content, and your business strategy—ready to get to work from day one. Our platform combines generative AI, human-in-the-loop workflows, and a living Enterprise Skill Graph that continuously learns and improves. Whether you're launching a product globally, onboarding new hires, translating learning content, or aligning legal teams across regions, Smartcat turns knowledge into action and action into scale. Over 1,000 companies, including 20% of the Fortune 500, rely on Smartcat to bring their business to the world—instantly, accurately, and in every language. As a Series C company with consistently high year-over-year growth, we’re scaling fast and investing in people who want to shape the future of work with us. Join us in unlocking global potential, one human and agent team at a time. WHY YOU SHOULD JOIN OUR TEAM You'll help build the intelligence layer that powers decision-making, AI agents, automation, and business operations across Smartcat — on a greenfield cloud data platform, transitioning from legacy on-premise, batch architectures to cloud-native, streaming-first infrastructure. This is a chance to shape how Smartcat scales its data capabilities over the next several years, directly aligned with the company's transformation into an AI-native, agentic platform. MISSION Help transform Smartcat's data platform into an AI-native foundation where business data becomes discoverable, actionable, and consumable by both humans and AI agents — partnering across Data, Product, Engineering, GTM, and AI teams to enable self-service analytics, intelligent automation, and agent-driven decision support. OUTCOMES Evolve Smartcat's data architecture into a scalable, cloud-native, AI-first platform, leading the transition from batch-oriented pipelines to near real-time and streaming systems Improve reliability, observability, governance, and performance across the data stack, establishing engineering standards and best practices Turn business data into AI-ready assets — building data products consumable by AI agents, analytics systems, and business users, with semantic layers and metadata management Use AI to accelerate development, testing, documentation, monitoring, and operational workflows, and design systems that let AI agents query, understand, and act on business data safely Improve business intelligence and data accessibility — making analytics simpler for non-technical stakeholders and enabling self-service without sacrificing governance or quality Raise the engineering bar through mentorship, code reviews, architecture leadership, and knowledge sharing across Data and Analytics Engineering HOW YOU'LL RAMP By Day 30: Get deep on Smartcat's current data architecture, the greenfield cloud platform build, and the transition plan from batch to streaming Meet your partners across Data, Product, Engineering, GTM, and AI teams to understand current pain points and priorities Get hands-on with the core stack (Databricks, dbt, Airflow, Python, SQL) and current BI tooling (e.g., Omni) By Day 60: Contribute to at least one concrete piece of the Next Generation Data Platform build (pipeline, streaming component, or data product) Have identified and started applying at least one AI-powered workflow to accelerate your own engineering work (testing, documentation, debugging) Begin shaping a semantic layer, metadata structure, or data product aimed at AI-agent or business-user consumption By Day 90: Own a defined piece of architecture or data product end-to-end, with measurable improvements in reliability, observability, or time-to-insight Be actively mentoring or raising the engineering bar through code reviews and architecture input Have a documented example of measurable productivity gains from AI adoption in your own workflow WHAT YOU'VE ACCOMPLISHED 6+ years of experience in Data Engineering, Analytics Engineering, or a related field A proven track record designing and operating modern cloud data platforms, ideally in high-growth SaaS environments Experience working effectively with both technical and business stakeholders Demonstrated ability to lead complex projects from design through delivery Strong hands-on experience with Databricks, dbt, Airflow, Python for data engineering, SQL and data modeling, data warehousing architectures, data quality frameworks, data governance, and data orchestration/integration Experience with streaming architectures and event-driven systems, business intelligence platforms, product analytics platforms, and CRM/customer data platforms A demonstrated AI-first mindset — AI-powered development workflows, automation of repetitive engineering tasks, use of AI for debugging/testing/architecture exploration/documentation, and evidence of measurable productivity gains through AI adoption Bonus: experience building AI-native data products, semantic layers, RAG systems, vector databases, knowledge graphs, or enabling AI agents to consume operational business data Why joining Smartcat might be your best move so far A global, connected team We’re a team of 200+ people across 30+ countries, working together through a hub-and-spoke model anchored in eight key locations: New York, London, Lisbon, Costa Rica, Serbia, Armenia, Georgia, and Spain.  Most of our roles are remote-friendly, giving you the flexibility to do your best work from where you thrive. For customer-facing teams, we prioritize proximity to our customers, ensuring we stay deeply connected to their needs and deliver an exceptional experience.  No matter where you’re based, you’ll be part of a highly collaborative, globally-distributed team that values ownership, speed, and meaningful impact. Be part of an AI Native Organization We are highly innovative, using AI across all areas of the organization to accelerate decision-making and free people to focus on strategy and high-impact work. We embrace new ideas and encourage all Smartcaters, regardless of level or department, to manage their own AI Agents. At Smartcat you’ll shape how AI transforms the workplace and play an integral role in ensuring Smartcat remains a leader in AI innovation. Innovating a $100 Billion industry Smartcat is reshaping the $100B multilingual content industry with an AI-powered platform that makes it easy for companies to create, translate, and localize global content at scale. Our platform enables enterprise teams to move away from slow,  traditional outsourcing methods, and achieve fast, high-quality results, at a fraction of the cost. Join the rocketship to scale-up 10x and beyond together We are looking for someone to become an integral part of our team and play a crucial role in the most exciting part of our journey: transitioning from a post-Series C startup to a company exceeding $100M in ARR and $1B in valuation. Our journey isn’t for the faint of heart. We are growing at 130% YoY, thanks to our strong product-market fit and high-performing team, and plan to accelerate from here. Smartcat Culture Code : Where Diversity Meets High Performance At Smartcat, we are committed to building a culture that highlights respect and appreciation for each individual's unique background and perspective, while maintaining a strong focus on results and engagement. We believe in welcoming everyone and fostering an inclusive environment where team members can be their authentic selves at work. Our commitment to inclusion is steadfast, and we stand firmly against discrimination and harassment.

Location: Cyprus Workplace: hybrid About the Team We are Quadcode, a fintech company excelling in financial brokerage activities and delivering advanced financial products to our global clientele. Our flagship product, an internal trading platform, is offered as a Software-as-a-Service (SaaS) solution to other brokers. We're looking for an AI Engineer to join our AI Platform team, which builds and operates the generative AI pipelines powering our products, many of which involve financial and trading data, terminology, and specifications. You'll join a small high-impact team where you'll have real ownership from day one. You'll work across our broader AI-platform portfolio: prompting and prompt evaluation, agentic systems, MCP tooling, and evaluation pipelines: writing backend Python on top of LLM providers like OpenAI, Anthropic, and xAI. This is an applied engineering role focused on shipping and maintaining production LLM systems. This role is hybrid, based in our Limassol office. Tasks: Design, build, and maintain LLM-powered features and pipelines for our generative AI products, including those handling financial and trading content Develop and improve agentic systems and multi-agent workflows, including integrations using MCP (Model Context Protocol) servers/tooling Write production-grade backend Python services on top of LLM provider APIs (OpenAI, Anthropic, xAI) and/or on-premise LLMs Build and iterate on prompt engineering and prompt-evaluation pipelines, including structured output generation and validation Take prototypes and proofs-of-concept and turn them into reliable, maintainable production services Monitor and manage LLM cost, latency, and reliability trade-offs across pipelines Collaborate closely with a small, senior AI Platform team on architecture, code review, and technical direction. Requirements: Production LLM experience: You've shipped real, live features using OpenAI and/or Anthropic APIs, or with on-premise/open-weight LLMs, not just experimentation or side projects Hands-on experience building multi-agent systems or workflows and working with MCP servers Strong Python & software engineering fundamentals (2–4+ years): REST APIs, SQL, Docker, git, and concurrency, with a demonstrated ability to take projects from prototype to maintainable production service Practical LLM engineering skills: Comfortable working with structured outputs, prompt evaluation, and cost-aware design decisions in real systems English fluency (written and verbal). Nice to Have: Prior experience working in fintech Finance/markets knowledge: tickers, earnings, technical indicators, forex/CFDs Quant background Experience with model fine-tuning/training, RAG, or LLM optimization techniques Track record in Kaggle competitions focused on LLMs or multi-agent frameworks GPU infrastructure experience: provisioning GPU compute or serving open-weight models (e.g., Hugging Face inference) Experience with Kubernetes, Kubeflow, or Airflow Data analysis and statistics experience Experience with cloud platforms (AWS, GCP, etc.) Fluent Russian. We know the combination of production LLM engineering and finance-domain knowledge is rare. If you're a strong AI/LLM engineer without a finance background, we still want to hear from you. What we offer: Hybrid work model in our brand-new office in Limassol. Health insurance and mental health services. 13th salary and 21 vacation days per year. Sick leave without medical certificate: 3 days per quarter. Catered lunches in the office. Tuition reimbursement (kindergartens/schools). Onsite Gym. Corporate events and workshops. Bonuses for special events (e.g., child's birth). Birthday and anniversary gifts. Company-provided laptop. Corporate AI subscriptions (Claude, Gemini, GPT, etc.). Access to a rewards marketplace offering products and language courses, redeemable using the company’s internal currency. With a team of 500+ creative and tech-driven people across the globe, Quadcode is the place where ideas turn into impact, where every project is a chance to grow, innovate, and make your mark. Join us today, and let's shape the future of fintech together! Note: All applications will be treated with strict confidence. We thank all applicants for their interest, however, only those candidates selected for interviews will be contacted. #LI-JM1

Location: Hybrid - Washington D.C Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. About the Program: Innodata's Federal Practice builds the trusted data layer for critical infrastructure Trust & Safety work. Partnering with a leading systems integrator, we're delivering a modern, governed data services platform in a secure federal (IL4) environment. Over an intensive 20-week phase, you'll help stand up a data services storefront, a DataCard governance framework, synthetic data integration, and Databricks write-back capabilities. About the Role: As the Solution Architect, you'll design the backbone of the platform. You'll define the architecture for the data services storefront and own the DataCard governance framework that keeps our data trustworthy, documented, and compliant end to end. Working closely with the engineering team and the Delivery Owner, you'll translate governance and security requirements in a secure federal (IL4) environment into a clean, defensible technical design. It's a role for an architect who cares as much about data stewardship and governance as about elegant systems. Key Responsibilities: Own cross-pillar architectural integrity and interface coordination across the 24-week program Design and stand up the DataCard governance framework: schema design, distribution-statement methodology (DoD Dir. 5230.24), provenance lineage Deploy and configure ZenML multi-cloud orchestration stack per Innodata Layer 2 Multi-Cloud Architecture v3 Design taxonomy engine and sequestration controls architecture Confirm NIST SP 800-53 Rev 5 control split between Innodata data-layer and AFS infrastructure-layer scope Lead platform component run books and per-DataCard documentation in Phase E Coordinate with AI Solutions Engineer on Kubernetes-adjacent data-layer DLP policy configuration Coordinate with Backend Engineer on Databricks write-back schema and DataCard write-back path Must-Have Qualifications: Bachelor's degree in Computer Science, Data Engineering, or related field required; Master's degree preferred. Equivalent experience may substitute for degree on a 2-for-1 basis. 10+ years total professional experience, 6+ years in data architecture or platform engineering Databricks, Delta Lake, and MLflow — hands-on implementation experience, not conceptual Data governance frameworks: DataCards, provenance, lineage, distribution statement controls ZenML or equivalent ML orchestration tooling (Airflow or Prefect acceptable; must be willing to ramp ZenML quickly) Working knowledge of IL4/IL5 boundary concepts, NIST SP 800-53 Rev 5, and DoD data classification requirements Active Secret clearance with TS/SCI eligibility Nice-to-Have Qualifications: Prior Innodata or annotation platform architecture experience MLflow model governance and registry hands-on experience Ontology and taxonomy design for AI/ML training datasets DoD Directive 5230.24 distribution statement implementation experience The expected hourly salary range for this position is $70 to $75 p/hour, based on experience, skills, and qualifications. Note to Candidates: This role is the primary technical decision-maker on the program below the Delivery Owner. The SA is expected to make architectural calls independently within the established framework and surface only unresolved cross-cutting decisions to the Delivery Owner. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Canada Workplace: Remote The Connectors team is the bridge between ClickHouse and the broader ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide, from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling. Our work directly shapes how companies process massive datasets: real-time analytics platforms that ingest millions of events per second, observability systems that monitor global infrastructure, and, increasingly, AI-powered data applications that redefine how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience. ABOUT THE ROLE As a Senior Software Engineer specializing in the AI & ML ecosystem, you'll be a core contributor owning and evolving critical parts of ClickHouse's AI ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use. We're looking for someone who has firsthand experience as an AI & MLEngineer or Data Scientist. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why. You'll own the full lifecycle of key AI/ML integrations, driving architecture, performance, and feature direction across AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications. ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real:  building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems. WHAT YOU'LL DO - Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design - Drive the AI/LLM integration strategy:  designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications - Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback - Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities - Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows ABOUT YOU - 7+ years of software development experience, including hands-on time as a Data Scientist or ML Engineer - Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation) - Hands-on experience applying AI/ML in production data-engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling that shipped and ran in production - Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries - Strong database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases - Solid experience with concurrent Python: threading, multiprocessing, and async patterns - Outstanding written and verbal communication; comfortable collaborating across engineering functions and with open-source communities Bonus points for: - Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role - Familiarity with ClickHouse or similar high-performance OLAP platforms - Familiarity with the JVM ecosystem - Experience deploying AI/ML models in production, including inference APIs and vector databases - Familiarity with tools like dbt, Airflow, Dagster, Prefect is a plus PERKS - Flexible work environment - ClickHouse is a globally distributed company and remote-friendly. We currently operate in over 25 countries. - Healthcare - Employer contributions towards your healthcare. - Equity in the company - Every new eligible team member who joins our company receives equity. - Time off - Flexible time off in the US, generous entitlement in other countries. - A USD$500 Home office setup if you’re a remote employee. - Global Gatherings – We believe in the power of in-person connection and offer opportunities to engage with colleagues at company-wide offsites. CULTURE - WE ALL SHAPE IT As part of a rapidly scaling start-up, you will be instrumental in shaping our culture. Are you interested in finding out more about our culture? Learn more about our values here https://clickhouse.com/company/careers. Check out our blog posts https://clickhouse.com/blog or follow us on LinkedIn https://www.linkedin.com/company/clickhouseinc/life/0c1ee7f4-2129-488d-b413-e7f6270659fd/?viewAsMember=true to find out more about what’s happening at ClickHouse. EQUAL OPPORTUNITY & PRIVACY ClickHouse provides equal employment opportunities to all employees and applicants and prohibits discrimination and harassment of any type based on factors such as race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Please see here https://clickhouse.com/legal/applicant-privacy-notice for our Privacy Statement.

Location: Germany Workplace: Remote The Connectors team is the bridge between ClickHouse and the broader ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide, from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling. Our work directly shapes how companies process massive datasets: real-time analytics platforms that ingest millions of events per second, observability systems that monitor global infrastructure, and, increasingly, AI-powered data applications that redefine how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience. ABOUT THE ROLE As a Senior Software Engineer specializing in the AI & ML ecosystem, you'll be a core contributor owning and evolving critical parts of ClickHouse's AI ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use. We're looking for someone who has firsthand experience as an AI & MLEngineer or Data Scientist. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why. You'll own the full lifecycle of key AI/ML integrations, driving architecture, performance, and feature direction across AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications. ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real:  building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems. WHAT YOU'LL DO - Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design - Drive the AI/LLM integration strategy:  designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications - Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback - Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities - Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows ABOUT YOU - 7+ years of software development experience, including hands-on time as a Data Scientist or ML Engineer - Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation) - Hands-on experience applying AI/ML in production data-engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling that shipped and ran in production - Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries - Strong database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases - Solid experience with concurrent Python: threading, multiprocessing, and async patterns - Outstanding written and verbal communication; comfortable collaborating across engineering functions and with open-source communities Bonus points for: - Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role - Familiarity with ClickHouse or similar high-performance OLAP platforms - Familiarity with the JVM ecosystem - Experience deploying AI/ML models in production, including inference APIs and vector databases - Familiarity with tools like dbt, Airflow, Dagster, Prefect is a plus PERKS - Flexible work environment - ClickHouse is a globally distributed company and remote-friendly. We currently operate in over 25 countries. - Healthcare - Employer contributions towards your healthcare. - Equity in the company - Every new eligible team member who joins our company receives equity. - Time off - Flexible time off in the US, generous entitlement in other countries. - A USD$500 Home office setup if you’re a remote employee. - Global Gatherings – We believe in the power of in-person connection and offer opportunities to engage with colleagues at company-wide offsites. CULTURE - WE ALL SHAPE IT As part of a rapidly scaling start-up, you will be instrumental in shaping our culture. Are you interested in finding out more about our culture? Learn more about our values here https://clickhouse.com/company/careers. Check out our blog posts https://clickhouse.com/blog or follow us on LinkedIn https://www.linkedin.com/company/clickhouseinc/life/0c1ee7f4-2129-488d-b413-e7f6270659fd/?viewAsMember=true to find out more about what’s happening at ClickHouse. EQUAL OPPORTUNITY & PRIVACY ClickHouse provides equal employment opportunities to all employees and applicants and prohibits discrimination and harassment of any type based on factors such as race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Please see here https://clickhouse.com/legal/applicant-privacy-notice for our Privacy Statement.

Location: Spain Workplace: Remote The Connectors team is the bridge between ClickHouse and the broader data ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling. Our work directly shapes how companies process massive datasets: real-time analytics platforms ingesting millions of events per second, observability systems monitoring global infrastructure, and increasingly, the AI-powered data applications redefining how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience. WHAT YOU'LL DO - Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design - Build and maintain integrations with orchestration platforms (Airflow, Dagster, Prefect) and transformation tools (dbt) to enterprise-grade quality standards - Drive the AI/LLM integration strategy:  designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications - Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback - Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities - Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows ABOUT YOU - 7+ years of software development experience, including hands-on time as a Data Engineer, Data Scientist, or ML Engineer - Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation) - Hands-on experience applying AI/ML in production data-engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling that shipped and ran in production - Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries - Strong database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases - Solid experience with concurrent Python: threading, multiprocessing, and async patterns - Outstanding written and verbal communication; comfortable collaborating across e The Connectors team is the bridge between ClickHouse and the broader ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide, from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling. Our work directly shapes how companies process massive datasets: real-time analytics platforms that ingest millions of events per second, observability systems that monitor global infrastructure, and, increasingly, AI-powered data applications that redefine how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience. ABOUT THE ROLE As a Senior Software Engineer specialising in the AI & ML ecosystem, you'll be a core contributor owning and evolving critical parts of ClickHouse's AI ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use. We're looking for someone who has firsthand experience as an AI & MLEngineer or Data Scientist. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why. You'll own the full lifecycle of key AI/ML integrations, driving architecture, performance, and feature direction across AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications. ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real:  building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems. WHAT YOU'LL DO - Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design - Drive the AI/LLM integration strategy:  designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications - Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback - Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities - Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows ABOUT YOU - 7+ years of software development experience, including hands-on time as a Data Scientist or ML Engineer - Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation) - Hands-on experience applying AI/ML in production data-engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling that shipped and ran in production - Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries - Strong database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases - Solid experience with concurrent Python: threading, multiprocessing, and async patterns - Outstanding written and verbal communication; comfortable collaborating across engineering functions and with open-source communities Bonus points for: - Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role - Familiarity with ClickHouse or similar high-performance OLAP platforms - Familiarity with the JVM ecosystem - Experience deploying AI/ML models in production, including inference APIs and vector databases - Familiarity with tools like dbt, Airflow, Dagster, Prefect is a plus PERKS - Flexible work environment - ClickHouse is a globally distributed company and remote-friendly. We currently operate in over 25 countries. - Healthcare - Employer contributions towards your healthcare. - Equity in the company - Every new eligible team member who joins our company receives equity. - Time off - Flexible time off in the US, generous entitlement in other countries. - A USD$500 Home office setup if you’re a remote employee. - Global Gatherings – We believe in the power of in-person connection and offer opportunities to engage with colleagues at company-wide offsites. CULTURE - WE ALL SHAPE IT As part of a rapidly scaling start-up, you will be instrumental in shaping our culture. Are you interested in finding out more about our culture? Learn more about our values here https://clickhouse.com/company/careers. Check out our blog posts https://clickhouse.com/blog or follow us on LinkedIn https://www.linkedin.com/company/clickhouseinc/life/0c1ee7f4-2129-488d-b413-e7f6270659fd/?viewAsMember=true to find out more about what’s happening at ClickHouse. EQUAL OPPORTUNITY & PRIVACY ClickHouse provides equal employment opportunities to all employees and applicants and prohibits discrimination and harassment of any type based on factors such as race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Please see here https://clickhouse.com/legal/applicant-privacy-notice for our Privacy Statement.

Location: Israel Workplace: Remote The Connectors team is the bridge between ClickHouse and the broader ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide, from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling. Our work directly shapes how companies process massive datasets: real-time analytics platforms that ingest millions of events per second, observability systems that monitor global infrastructure, and, increasingly, AI-powered data applications that redefine how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience. ABOUT THE ROLE As a Senior Software Engineer specializing in the AI & ML ecosystem, you'll be a core contributor owning and evolving critical parts of ClickHouse's AI ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use. We're looking for someone who has firsthand experience as an AI & MLEngineer or Data Scientist. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why. You'll own the full lifecycle of key AI/ML integrations, driving architecture, performance, and feature direction across AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications. ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real:  building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems. WHAT YOU'LL DO - Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design - Drive the AI/LLM integration strategy:  designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications - Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback - Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities - Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows ABOUT YOU - 7+ years of software development experience, including hands-on time as a Data Scientist or ML Engineer - Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation) - Hands-on experience applying AI/ML in production data-engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling that shipped and ran in production - Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries - Strong database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases - Solid experience with concurrent Python: threading, multiprocessing, and async patterns - Outstanding written and verbal communication; comfortable collaborating across engineering functions and with open-source communities Bonus points for: - Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role - Familiarity with ClickHouse or similar high-performance OLAP platforms - Familiarity with the JVM ecosystem - Experience deploying AI/ML models in production, including inference APIs and vector databases - Familiarity with tools like dbt, Airflow, Dagster, Prefect is a plus PERKS - Flexible work environment - ClickHouse is a globally distributed company and remote-friendly. We currently operate in over 25 countries. - Healthcare - Employer contributions towards your healthcare. - Equity in the company - Every new eligible team member who joins our company receives equity. - Time off - Flexible time off in the US, generous entitlement in other countries. - A USD$500 Home office setup if you’re a remote employee. - Global Gatherings – We believe in the power of in-person connection and offer opportunities to engage with colleagues at company-wide offsites. CULTURE - WE ALL SHAPE IT As part of a rapidly scaling start-up, you will be instrumental in shaping our culture. Are you interested in finding out more about our culture? Learn more about our values here https://clickhouse.com/company/careers. Check out our blog posts https://clickhouse.com/blog or follow us on LinkedIn https://www.linkedin.com/company/clickhouseinc/life/0c1ee7f4-2129-488d-b413-e7f6270659fd/?viewAsMember=true to find out more about what’s happening at ClickHouse. EQUAL OPPORTUNITY & PRIVACY ClickHouse provides equal employment opportunities to all employees and applicants and prohibits discrimination and harassment of any type based on factors such as race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Please see here https://clickhouse.com/legal/applicant-privacy-notice for our Privacy Statement.