Вакансии

Location: Paris, France Workplace: OnSite HEALTH CAN’T WAIT. Not for symptoms to get worse. Not for a six‑month appointment. Not for a system to catch up. But that’s exactly how healthcare works today. You wait, until you can’t. Alan exists to end the wait. Health is a universal right, and we believe this right can only become real when it’s coupled with prevention. We need to stop treating health as something we repair and start treating it as something we build, every day. It’s not solely a question of willpower. It’s the healthcare system itself that needs to work for everyone, in a sustainable way. So we are building the new standard in prevention insurance. Alan is the first company that integrates insurance, prevention, and care into a single, acclaimed user experience. We are on an incredible journey to build a global leading company, with a unique culture https://alan.com/en/careers. We already partner with 40K+ companies of all sizes, serving more than 1M+ members, and have reached €800M+ in ARR. Prevention as the new norm. That's what we're building with our team of 1000+ people. If it speaks to you: we're hiring across France, Spain, Belgium, and Canada. And beyond. ⭐ DATA ASSOCIATE ⭐ The position is a six-month end-of-studies internship in Paris, with a flexible start date. Data Associates join a product or business crew and work alongside a senior member of the Data team, taking on a real share of their projects. It is the same work as the rest of the team, building insights and data-powered products that help our members stay healthy, at a pace and scope designed for someone starting out. Our team is full stack: each member sits in a crew next to engineers, product builders and designers and leads projects from A to Z, covering analytics, engineering and science. We mainly use SQL, Python, dbt, Airflow and Metabase, and Claude Code and AI agents are part of our daily workflow. Want to know more? Read our vision for the Data team https://medium.com/alan/vision-for-a-data-team-2eae845b8052 (old one, but still relevant) and how our Data team is re-inventing its craft with AI https://medium.com/alan/re-inventing-our-craft-how-alans-data-team-is-shaping-its-future-with-ai-e8d73d095ece. This is our first generation of Data Associates, and we built the program with one goal: you join us full-time at the end of it. That decision is made at your 5-month review. ⭐ DETAILED MISSIONS ⭐ What you work on depends on the crew you join, something that is decided once you start your internship, but your six months will mix: - Answering real business or product questions: framing the problem, digging into the data and turning what you find into a recommendation your crew acts on. - Building tools people use: a dbt model, a dashboard a team uses every week, a tool powered by AI. You would ship them end to end, with your senior counterpart supporting you on key steps - Growing with the Data community: joining our main rituals (retros, insights sharing sessions, on-call shifts), and later helping us recruit the next generation of Data Associates You won’t be alone: from day one, you will benefit from a senior Alaner from the Data team to work with at the office and a coach from the Data team as well. ⭐ IS IT YOU WE'RE LOOKING FOR? ⭐ Please note that for this internship, you need to: - Be based in Paris, France - Work from our Valmy office at least 3 days a week - Be in the final year of your studies (engineering or business, with a strong quantitative track), with an internship agreement from your school You would be a great fit if most of this sounds like you: - You want a career in Data, across the whole spectrum. If what you want above everything else is to build ML models, a research internship is probably a better fit for you - You have used SQL and Python, at school, in an internship or on a side project. We teach you the rest - Analytical sense and rigor: you ask the right question before you reach for the data, and you can tell a real signal from noise - Business & product sense: you care about what the numbers change for members and for the company - Effective communication: you explain a result simply, in writing and out loud - High agency and growth mindset: facing an unfamiliar problem, your first instinct is to find a way through, and you ask for feedback to grow - AI fluency: you already build with AI tools, and you are curious about where they fall short - You are fluent in English (no need to speak French!) But above all, we are looking for high potential and curiosity: make sure to show us this when you apply! Everything else is a bonus. For this internship, we are hiring at the A2 level of our salary grid https://alan.com/en/careers/a/salary-equity. 🎯 IMPORTANT NOTE: WE HIRE PEOPLE, NOT ROLES If you're excited about this opportunity but don't check every box, we'd love to hear from you. Everyone, no matter how underrepresented, should feel free to apply as it can only bring learnings or success. If you identify yourself as a woman: Did you know that research shows women often apply only when meeting 100% of requirements? Remember, this is just a guide, not a checklist. We'll be thrilled to receive your application! 🔖 Check out our About Alan https://alan.com/about and Career https://alan.com/careers pages, as well as our Medium https://medium.com/alan, blog https://blog.alan.com/ and Glassdoor page https://www.glassdoor.com/Overview/Working-at-Alan-EI_IE1696043.11,15.htm for more info. YOU WANT TO KNOW MORE ABOUT ALAN? - 🙌 Perks & Benefits https://alan.com/en/careers/a/salary-equity: Alaners are provided with a stimulating environment and perks ensuring they are happy, efficient and spend only high-quality time with co-workers. - 🤘 A strong culture https://alan.com/en/about/a/leadership-principles: people joining Alan are often surprised and delighted by our innovative working method. We have a set of cultural values that guide our approach to work. #LI-DNP

-Разработка моделей данных и отчетных решений в Power BI и Apache Superset с учетом бизнес-задач.

-Сбор данных из внутренних сервисов, LLM Gateway, GitLab, Jira, логов AI-агентов и других источников. -Проектирование и наполнение баз данных и аналитических витрин для оценки использования CodeGen-платформы.

Flow Traders is looking for a Senior Software Engineer Post Trade Platform to build reliable, automated, and data-driven post-trade services.

3+ лет опыта работы в Data Engineering. Сильные навыки SQL и производственного уровня Python.

Location: Москва Мы - команда GigaChat Pretrain Data, готовим pretrain данные для GigaChat и GigaChat Vision. Pretrain данные - это фундамент, с которого начинается путь современной LLM модели и то, от чего наиболее зависит ее итоговое качество. Сырых данных более 40Пб и основная задача заключается в том, чтобы из этого хаоса сделать датасет, на котором будет обучена лучшая LLM в России. Обязанности Основные задачи будут лежать в следующих сферах: Инфраструктура: * поддержка данных для обучения VLM на кластере YTSaurus. * построение удобной платформы для эффективного анализа/обработки данных (фильтрация, дедупликация и пр.) * автоматизация процессов через Airflow. RnD: * поиск и синтез данных для обучения VLM (есть несколько областей: OCR/Charts&Tables/Grounding&Counting/General и тд). * исследование пайплайнов по созданию чистых и разнообразных датасетов (на примере FineVision). Эксперименты и метрики: * Обучение VLM на подготовленных данных. * проведение исследований релевантных intrain метрик, бенчмарков для замера качества. Требования * Опыт построения дата пайплайнов и data quality процессов * Опыт работы в production ml команде (большие нагрузки как преимущество) * Понимание задач CV и современных подходов в больших языковых моделях * Опыт решения задач, связанных с влиянием данных на качество VLM * Практический опыт работы с VLM. Условия * комфортный современный офис рядом с м. Кутузовская * возможность выбрать удобный график – офис/гибрид/удаленка (в РФ) * годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * ипотека для сотрудников по дисконтной прогамме * бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров * вознаграждение за рекомендацию друзей в команду Сбера.

Location: Bangalore, India GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role As a Data Engineer at GitLab, you'll build, run, and maintain the pipelines and infrastructure that bring data from our enterprise applications, internal systems, and GitLab's software as a service (SaaS) platform into our data platform. Your work will power company reporting, data science, and artificial intelligence (AI) self-service. You'll own features end to end, from source integration to tested, monitored, documented data ready for use. You'll focus on keeping the platform reliable, secure, and cost-efficient as data volumes and AI usage grow, and making governed data ready for AI tools, agents, and Model Context Protocol (MCP)-based access. This is an AI-first role: you'll use AI coding agents every day while maintaining high standards for code review and testing. You'll report to the Director, Data Platform. What you’ll do Build, extend, and maintain extract, load, transform (ELT) pipelines from enterprise applications such as Salesforce, Zuora, Zendesk, and Marketo, internal systems, and the GitLab SaaS platform. Integrate data through application programming interfaces (APIs) and event sources such as Snowplow. Manage and maintain the infrastructure behind the platform, including SaaS tools such as Snowflake, Fivetran, and Monte Carlo, and self-managed services such as Airflow and Kubernetes. Write maintainable, well-tested, performant Python and Structured Query Language (SQL) code that meets our internal standards, and review GitLab team members' merge requests. Own day-to-day platform operations: triage and resolve failed pipelines, infrastructure issues, and GitLab team member requests. Use monitoring, service-level agreements (SLAs), data contracts, query and warehouse tuning, and data quality checks to keep the platform reliable and cost-efficient. Partner with Analytics Engineers, Data Analysts, Data Scientists, Data Governance, and business partners so data lands in the shape they need for downstream use. Use AI coding agents such as Claude Code, OpenCode, GitLab Duo, or similar tools to speed up development, debugging, testing, and documentation without compromising review rigor. Apply role-based access control (RBAC) and masking policies to make governed data available to AI tools and agents. Handle data subject to Sarbanes-Oxley (SOX) controls, material nonpublic information (MNPI), and personal data safely. Ship medium-sized features independently in small, iterative merge requests, and document architecture and runbooks in the handbook. What You'll Bring Professional experience shipping production data pipelines in Python. Deep, hands-on SQL skills and experience with a cloud data warehouse; Snowflake is preferred. Hands-on experience with ELT or extract, transform, load (ETL) pipelines at scale, including incremental loads, schema changes, backfills, and API ingestion. Experience with orchestration tools such as Airflow, familiarity with dbt, and solid data modeling fundamentals, including Kimball dimensional modeling. Experience running and troubleshooting data infrastructure, with comfort using Git-based workflows, continuous integration and continuous delivery (CI/CD), and code review. Hands-on experience using AI coding tools in your daily work, with an understanding of where they help and where they fall short. Clear written communication, the ability to use GitLab, and comfort working asynchronously in a fully remote organization in line with GitLab's values. Preferred, not required: experience with Snowflake; Docker, Kubernetes, Google Cloud Platform (GCP), or Terraform; SaaS application APIs such as Salesforce, Zuora, or Zendesk; data observability tooling; or Snowflake administration. About the team The Data Platform team within Enterprise Data is responsible for a scalable, reliable and secure data platform for data analytics at GitLab. That includes managing and maintaining the infrastructure we use, both SaaS and self-managed, and building and maintaining the data pipelines that bring data into it. We make data reliable, secure and ready for analytics, data science and AI tools. As more questions go straight to AI tools and agents, the platform underneath has to be more reliable, better governed and more cost-efficient than ever. We're fully remote and globally distributed, and work asynchronously through issues, merge requests and the handbook. How GitLab Supports Full-Time Employees Benefits to support your health, finances, and well-being Flexible Paid Time Off Team Member Resource Groups Equity Compensation & Employee Stock Purchase Plan Growth and Development Fund Parental Leave Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application. Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process. Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us. GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law . If you have a disability or special need that requires accommodation , please let us know during the recruiting process .

Location: China We are seeking an AI Engineer to join our Data & AI team and contribute to the development and continuous improvement of our AI Validation platform. Who we are: Founded in 2006, CXG is a global customer experience company uniquely positioned at the intersection of data-driven retail performance, customer experience transformation, and operational execution across 200+ luxury brands worldwide. The mission of our 270 team members is to help brands transform customer and employee experiences through tailored insights, strategy, and implementation. We are passionate about delivering impact. Our clients include the world’s most admired luxury houses, and our solutions combine research, technology, and human expertise to elevate every customer interaction. What will your role be: This role focuses on evaluating, deploying, monitoring, and optimizing AI models used to process and analyze multilingual content. Given the nature of the project, a deep understanding of both Mandarin Chinese and English language nuances is critical. Therefore, native-level proficiency in Mandarin Chinese and advanced proficiency in English are mandatory. The ideal candidate combines strong AI engineering skills with a pragmatic, production-oriented mindset and is passionate about delivering reliable and measurable AI solutions. What you will be doing: AI Development & Deployment Develop, deploy, and maintain AI-powered applications and services. Integrate and operationalize machine learning and generative AI models. Monitor model performance, quality, and reliability in production. Troubleshoot and resolve issues related to AI systems and model outputs. Continuously improve model performance through testing, evaluation, and optimization. AI Validation & Quality Assurance Contribute to the development of the AI Validation platform. Evaluate AI-generated outputs for accuracy, consistency, and linguistic quality. Design and implement validation methodologies and evaluation metrics. Benchmark different AI models and approaches. Analyze model failures and recommend improvements. Collaboration Work closely with Data Scientists, Data Engineers, and business stakeholders. Translate business requirements into scalable AI solutions. Document technical solutions and best practices. Requirements Technical Skills Advanced proficiency in Python. Strong proficiency in SQL. Solid experience with Git and collaborative software development practices. Hands-on experience deploying and maintaining machine learning or generative AI models in production. Experience working with Large Language Models (LLMs) and modern AI frameworks. Sound understanding of model evaluation, testing, and performance monitoring. Language Skills (Mandatory) Native-level Mandarin Chinese. Advanced English. Ability to assess linguistic quality and cultural nuances across both languages. Professional Attributes Strong analytical and problem-solving skills. Attention to detail and commitment to quality. Ability to work independently and collaboratively in a fast-paced environment. Excellent communication skills. Nice to Have Familiarity with Snowflake. Experience with dbt and Airflow. Experience with RAG architectures, agent-based systems, or AI evaluation frameworks.

Построение Lakehouse для работы с файловыми и табличными данными, создание надежных потоков данных для промышленной эксплуатации. - Практический опыт проектирования и построения Lakehouse.

Обязанности: Задачи: сбор, согласование и формализация требований бизнеса к отчетности и метрикам; написание SQL витрин данных и запросов;

- Проектирование архитектуры хранилищ, моделей данных и стратегий партиционирования. - Разработка DAG, кастомных операторов и интеграций на Python с использованием Apache Airflow.

Задачи: — Анализировать эффективность performance-маркетинга, выявлять точки роста, оптимизировать рекламные бюджеты, внедрять и масштабировать умную атрибуцию. — Проектировать, запускать и оценивать результаты A/B-тестов для проверки маркетинговых гипотез. — Плотно взаимодействовать с командой маркетинга и аналитиками для настройки корректной разметки и сквозной воронки, а также выступать заказчиком для дата-инженеров при постановке задач на доработку хранилища данных и ETL-пайплайнов. Требования: — Опыт работы в маркетинговой или продуктовой аналитике не менее 3 лет. — Глубокое понимание маркетинговых метрик и юнит-экономики, включая ROMI, ROAS, CAC, LTV, CPO, ДРР, Retention Rate, Churn Rate и конверсии на всех этапах пользовательского пути. — Умение анализировать показатели, выявлять взаимосвязи, искать аномалии и делать бизнес-выводы. — Уверенное владение SQL (сложные запросы, оконные функции, оптимизация). — Хорошее владение Python (Pandas, NumPy, SciPy) для аналитических задач и статистической оценки экспериментов — преимущество. — Опыт работы с системами веб и мобильной аналитики (Яндекс.Метрика, AppMetrica), понимание мобильного трекинга и логики диплинков. — Способность аргументированно защищать выводы и проактивно искать инсайты. — Опыт работы с AppsFlyer будет плюсом. — Понимание устройства пайплайнов данных, базовые знания Airflow (умение читать чужой код и логи). Условия: — Офис у ст. м. Шелепиха с гибридным или удалённым форматом работы. — Официальное оформление по ТК РФ. — Расширенный социальный пакет (ДМС и стоматология). — Перспективы профессионального и карьерного роста. — Скидки в сетях Inventive Retail Group и компаниях-партнёрах, участие в конкурсах с призами.

Location: Ukraine; Spain; Slovakia; Serbia; Yerevan; Slovenia; Poland Workplace: remote 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. 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 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. 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 engineering session is also possible

Location: Limassol, Cyprus Workplace: fully Sumsub is the first AI-powered trust infrastructure for compliance operations at scale. It connects identity and business verification, fraud prevention, transaction monitoring, and risk workflows, helping teams reduce manual work and enter new markets. Trusted by over 4,000 clients across financial services, crypto, mobility, trading, marketplaces, education, and iGaming, including Bybit, Bitpanda, Wirex, Avis, Vodafone, Duolingo, and Kaizen Gaming. Sumsub is certified as a Great Place To Work® in the US, recognized as a TIME & Statista Excellent Workplace, honored with the TrustRadius Tech Cares Award for Diversity & Inclusion, and rated 4.7 out of 5 on Glassdoor. Our 1,000+ people around the world back it up, too: 92% are proud to be Sumsubers, and 90% say their work is interesting and challenging. Now we are looking for a Senior Backend Developer to join our rapidly growing team. This is an amazing opportunity to join Sumsub and make an immediate and real impact on the future of our product. Our Stack: Main language: Java 17 Java Framework: Dropwizard / Restler DB: MongoDB / ClickHouse / Redis Others: Apache Kafka / Kafka Streams / Elasticsearch / OpenSearch (k-NN) / Airflow What You Will Be Doing: Development of data processing algorithms related to remote identification / authentication of individuals (both product and technical). Improvement and development from scratch of internal tools for compliance officers. Test driven development and A/B testing. Optimization and automation of CI/CD pipelines to increase reliability and deployment speed. Monitoring, troubleshooting, and performance tuning of cloud infrastructure and distributed systems. About You: Good command of the English language Familiarity with our development stack Understanding of product and a passion for building software that provides a great experience Experience developing clean, high-quality code and automated tests to verify its operation Team player, strong communication skills, and empathy Demonstrated strong analytical and problem-solving skills What We Offer: Remote-first, trust-based culture. Work from the place that works best for you. No mandatory office days, no attendance trackers. In some locations, we provide offices or coworking spaces, but the choice is yours. True flexibility. We do not fix you to a 9-to-5 schedule. You can adjust your working hours when needed, as long as your day stays productive and in sync with the team. Extra time off. Your birthday is a holiday here. Add to that 10 personal days each year, seven sick days without paperwork, and extra time to enjoy Christmas and New Year. Time to rest is part of the deal. Work that matters. Our mission is to build a digital world that is secure, accessible and inclusive for everyone. From fighting fraud to making online services easier and safer to use, your work will have a real impact on how people experience trust online. Compensation. We offer fair and transparent pay, benchmarked to the market. Truly global. We work across continents and time zones, with teammates and customers from all over the world. You will run campaigns that cross borders, cultures, and languages, and see your ideas land worldwide. Growth built in. Clear goals, open feedback and personal development plans. We support your progress with learning opportunities and by covering role-specific events, from design conferences to marketing forums. Celebrating your big moments. We are proud to support you through life’s biggest journeys with dedicated financial bonuses for both marriage and welcoming a new baby to your family. Team offsites. Sometimes just Slack is not enough. That is why we meet in person a few times a year. Trips are fully covered, so you can meet, collaborate, and recharge together. Getting you set up. We make sure you have access to the tools and hardware you need to do your work well. Friendly by design. Our logo is a dog for a reason. We keep things human, open and kind. We welcome individuality, quirks and different perspectives, because that is what makes our work smarter and more fun. The hiring stages: TA screening -> Hiring Manager Interview -> Assignment -> Final Interview. Sounds like a great opportunity for your career development? Then go ahead and apply! We are a global community of innovators, creators, and thinkers, and we believe that diversity fuels our innovation. Sumsub is proud to be an equal opportunity employer, committed to building a diverse and inclusive workforce. We welcome applications from people of all backgrounds, cultures, genders, experiences, abilities and perspectives. Join us in shaping the future inclusively. We believe in fair and transparent compensation. Salaries vary depending on work location and applicable local market conditions. This role is open to candidates in multiple countries, and compensation differs based on the candidate’s confirmed work location. For this reason, a single universal salary range is not included in this job posting. The applicable work location will be confirmed early in the recruitment process, and the relevant compensation information for that location will be shared in writing, in line with local requirements.

Location: North Macedonia; Bosnia and Herzegovina; Czechia; Croatia; Moldova; Bulgaria; Montenegro Workplace: remote 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. 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 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. 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 engineering session is also possible

в задачи входит разработка и поддержка DAG’ов в Apache Airflow, настройка CDC‑процессов для передачи данных в аналитическое хранилище, создание и оптимизация витрин данных на SQL, реализация трансформаций на Apache Flink, контроль качества данных инструментами Data Quality и оптимизация их обработки.

Location: Ukraine; Serbia; Yerevan; Spain; Slovakia; Slovenia; Poland Workplace: remote 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 FDX; our delivery arc is Sprint → Enable → Realize: Forward Deployed Executive (FDX) owns the commercial relationship and the business outcome. Works alongside the client's leadership or C-suite level to move the client's KPIs — revenue growth, cost reduction, risk reduction. Forward Deployed Engineer (FDE) embeds with a client to change how that client operates. You own the method; nobody hands you a ticket. You map the client workflow as it actually happens, identify the business problem underneath it, build a working AI solution, present to the client in the language of outcomes, and transfer it. Provectus maintains industry Blueprints — working systems that have already shipped for a client in the same industry — so you begin from running code and tuning it to this client's specific book, regulators, and operating posture. You will be measured on whether the Business Unit's number moved, not on hours or scope delivered. What You’ll Do: Take the seat Sit with the client and the Forward Deployed Executive at the start of an engagement. Learn the function from inside, not from a requirements doc, and redesign the function from first principles. Reach working fluency in a new domain — insurance underwriting, healthcare revenue cycle, asset flow. Build Design and ship production GenAI systems into the customer’s environment (cloud-native data, LLM-based, and agentic AI solutions). Implement and optimize RAG systems for production use cases Build the evaluation harness before you build the feature. Define what working means, instrument it, and let the evals drive the design. Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer. Take systems to production on AWS (GCP or Azure where the customer requires it): containerised, CI/CD, automated testing, monitoring, and maintainable after we leave. Hand the system over to the client. Start from the blueprint, and feed the blueprint. What you learn in the field becomes the baseline the next engagement starts from. Lead architecture reviews, produce technical design documents, and contribute to standards. Mentor engineers and share knowledge across the team. Own the outcome. Work in a pair with a FDX who carries the Business Unit’s KPIs. Your work is measured against the same number. Own the technical direction of technical proposals and scoping. Drive adoption. Change management is part of the engineering job here. Be credible with the customer’s engineers and their executives. Shape what we commit to before we commit to it. What You’ll Bring: Mindset Proactive and self-directed; identify problems before they're handed to you Comfort with ambiguity and ownership. Engagements start underspecified by design. Closing that gap is the job B2+ English, comfortable collaborating across distributed, multicultural teams Client Engagement You are willing to spend time understanding and doing someone else’s job on the client's side before you write a line of code Credible with senior stakeholders — you can hold a redesign conversation with a BU head and a scoping conversation with a CTO, presenting outcomes to them You can produce a scoped, phased delivery plan with clear deliverables, dependencies, and risks — and estimate what it will cost to build and to run Technical depth 7+ years building and running production systems. Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes Designed and 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 — dropped into an unfamiliar codebase or language, you’re productive. Python and/or TypeScript proficiency; depth matters more than stack. Experience in making and defending architectural trade-off decisions Hands-on AWS production depth: 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 Nice to have: Prior experience as a founder, CTO, or engineering leader who has chosen to return to individual contribution Experience in one of the industries: financial services, insurance, healthcare Consulting, professional services, or other embedded customer-facing delivery A2A: you can explain agent-to-agent interoperability AWS and Claude Code Certifications CI/CD pipeline experience (GitHub Actions, GitLab CI) 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 Principal Architects and Forward Deployed Engineers 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 engineering session is also possible

Location: North Macedonia; Montenegro; Moldova; Bulgaria; Czechia; Bosnia and Herzegovina; Croatia Workplace: remote 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 FDX; our delivery arc is Sprint → Enable → Realize: Forward Deployed Executive (FDX) owns the commercial relationship and the business outcome. Works alongside the client's leadership or C-suite level to move the client's KPIs — revenue growth, cost reduction, risk reduction. Forward Deployed Engineer (FDE) embeds with a client to change how that client operates. You own the method; nobody hands you a ticket. You map the client workflow as it actually happens, identify the business problem underneath it, build a working AI solution, present to the client in the language of outcomes, and transfer it. Provectus maintains industry Blueprints — working systems that have already shipped for a client in the same industry — so you begin from running code and tuning it to this client's specific book, regulators, and operating posture. You will be measured on whether the Business Unit's number moved, not on hours or scope delivered. What You’ll Do: Take the seat Sit with the client and the Forward Deployed Executive at the start of an engagement. Learn the function from inside, not from a requirements doc, and redesign the function from first principles. Reach working fluency in a new domain — insurance underwriting, healthcare revenue cycle, asset flow. Build Design and ship production GenAI systems into the customer’s environment (cloud-native data, LLM-based, and agentic AI solutions). Implement and optimize RAG systems for production use cases Build the evaluation harness before you build the feature. Define what working means, instrument it, and let the evals drive the design. Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer. Take systems to production on AWS (GCP or Azure where the customer requires it): containerised, CI/CD, automated testing, monitoring, and maintainable after we leave. Hand the system over to the client. Start from the blueprint, and feed the blueprint. What you learn in the field becomes the baseline the next engagement starts from. Lead architecture reviews, produce technical design documents, and contribute to standards. Mentor engineers and share knowledge across the team. Own the outcome. Work in a pair with a FDX who carries the Business Unit’s KPIs. Your work is measured against the same number. Own the technical direction of technical proposals and scoping. Drive adoption. Change management is part of the engineering job here. Be credible with the customer’s engineers and their executives. Shape what we commit to before we commit to it. What You’ll Bring: Mindset Proactive and self-directed; identify problems before they're handed to you Comfort with ambiguity and ownership. Engagements start underspecified by design. Closing that gap is the job B2+ English, comfortable collaborating across distributed, multicultural teams Client Engagement You are willing to spend time understanding and doing someone else’s job on the client's side before you write a line of code Credible with senior stakeholders — you can hold a redesign conversation with a BU head and a scoping conversation with a CTO, presenting outcomes to them You can produce a scoped, phased delivery plan with clear deliverables, dependencies, and risks — and estimate what it will cost to build and to run Technical depth 7+ years building and running production systems. Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes Designed and 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 — dropped into an unfamiliar codebase or language, you’re productive. Python and/or TypeScript proficiency; depth matters more than stack. Experience in making and defending architectural trade-off decisions Hands-on AWS production depth: 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 Nice to have: Prior experience as a founder, CTO, or engineering leader who has chosen to return to individual contribution Experience in one of the industries: financial services, insurance, healthcare Consulting, professional services, or other embedded customer-facing delivery A2A: you can explain agent-to-agent interoperability AWS and Claude Code Certifications CI/CD pipeline experience (GitHub Actions, GitLab CI) 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 Principal Architects and Forward Deployed Engineers 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 engineering session is also possible