Данные и ML вакансии - удалённо

Banco Plata is a digital bank and fintech startup specializing in consumer lending, credit cards with a cashback program, and interest-free installment plans. Remote work or relocation options available to Cyprus, Serbia, Spain, Georgia, or Mexico.

Responsibilities: • Design multi-agent systems and integrate LLMs with internal tools • Develop backend APIs, RAG pipelines, and tool/function calling • Build computer vision pipelines for detection, tracking, and re-identification • Optimize real-time video and GPU inference Requirements: • Node.js/Express.js and Python • LLM APIs, prompt engineering, RAG, tool calling, or agent frameworks • Docker, Linux, and Git • PyTorch and OpenCV • Object detection and multi-object tracking (YOLO, ByteTrack/BoT-SORT) • Person re-identification, vector search, and video streaming (RTSP)

Plata is a Mexican fintech startup specializing in consumer lending through the Plata Card. Remote work. Flexible working hours. Relocation support (Serbia, Georgia, Mexico or Colombia).

Задачи: — Design and build AI/ML-powered features and systems — Work with LLMs, APIs, and model integrations — Develop and optimize AI pipelines for real-world use cases — Collaborate with product and engineering teams — Turn complex AI ideas into scalable production features Требования: — Strong experience with Python and AI/ML frameworks — Hands-on experience with LLMs (OpenAI, LangChain, etc.) — Understanding of model integration, prompting, or fine-tuning — Experience with APIs and backend systems — Strong problem-solving mindset and startup experience

Location: Colombia Workplace: remote About the Project  Project Hydrus is a scripted and conversational audio data collection project. We're looking for native-level speakers who can deliver natural, everyday-sounding speech — recordings that hold up whether you're reading a script or speaking spontaneously.  Please note: the recordings you produce may be used to create synthesized, AI-generated voices for conversational AI products. Full details on how your data is collected, used, shared, and retained are set out in a Consent Form, which you'll review and sign before any paid session recording begins.  What You'll Do If Selected:      - Two supervised sessions of approximately 4 hours each, guided in real time by a Session Director on a separate recording platform.      - Record scripted material in a natural, conversational tone — faithfully, without a polished, performed, or “announcer” delivery.      - Record unscripted, spontaneous speech on a given topic, matching the same warmth and pace as your scripted takes.      - Set up and manage your own recording space and equipment for session recording.      - Deliver all recordings entirely in the required language (with specific dialect), without code-switching into other languages or dialects.  What We're Looking For  Required      - Experience as a voice actor, actor, or theater performer, or a comparable performance background.      - Native or native-equivalent fluency in the required language (with specific dialect).      - Access to a quiet recording space, free of echo and background noise (acoustically treated for session recording, if selected).      - Owns or will acquire a dedicated, professional-grade microphone for the audition (e.g. RODE NT-USB Mini, Blue Yeti), upgrading to a qualifying USB condenser or XLR mic + audio interface for session recording if selected. Built-in laptop mics, AirPods, earbuds, and headsets are not accepted at any stage.      - Owns closed-back monitoring headphones (required for session recording if selected).      - Comfortable both self-directing for the audition and taking live direction from a Session Director in guided sessions.  Preferred (nice to have, not required)      - Professional voice-over experience (dubbing/ADR, audiobook, podcasting) in a conversational rather than commercial/announcer register.      - Baseline audio hygiene: comfortable setting mic distance and catching noise, clipping, or distortion yourself.      - An existing home recording setup already treated for sound.  Your Data & Privacy  Before any session recording, you'll be asked to review and sign a Consent Form covering how your voice recordings are collected, used (including to train synthesized/AI voice models), shared with approved partners, retained, and how to exercise your data rights (access, correction, deletion, and more, depending on where you live). If you'd like to review these terms before applying, contact us and we'll share a copy in advance.  #L1-CC1

Задачи: — улучшение RL-моделей, обучение трансформеров для извлечения информации о взаимодействии участников движения с траекторией автомобиля — проверка и внедрение современных подходов из научных статей об автономном вождении — экспериментирование с наградами и методами обучения — внедрение разработок в пайплайн движения автомобиля и наблюдение за повышением качества технологии Требования: — опыт обучения нейросетевых моделей и внедрения DL-решений в продакшен — знание области DL и способность быстро разбираться в новых методах — программирование на Python, опыт работы с PyTorch — знание классических структур данных и алгоритмов — умение выдвигать гипотезы и выстраивать эксперименты для их проверки — чтение статей о ML и следование развитию области Дополнительные требования: — опыт внедрения методов обучения с подкреплением в продакшен — публикации в сборниках научных конференций высокого уровня — опыт работы с большими объёмами данных — участие в соревнованиях по ML или спортивному программированию — увлечение автомобилями или автономными автомобилями

Location: Remote - Global Workplace: Remote About the company Trust Wallet is the secure, self-custody Web3 wallet that empowers people to own and control their digital assets. Whether you're new to crypto or an experienced user, Trust Wallet makes it easier, safer, and more convenient to explore Web3, securely access dApps, manage your crypto, and buy and sell digital assets, all in one place. ABOUT THE ROLE As a Senior Data Engineer, you will play a key role in building and scaling the data platform that powers analytics across Trust Wallet. You'll be responsible for designing, developing and maintaining data pipelines and data models, ensuring a seamless, scalable and reliable flow of data from source systems through to the decisions it supports. Leveraging your experience with Databricks and Spark across both streaming and batch workloads, dbt or a similar transformation framework, cloud infrastructure, and custom solutions in Python, you'll work closely with our data analysts and engineering teams to turn data into a strategic asset for our company. KEY RESPONSIBILITIES - Data Platform Engineering: Architect and maintain robust, scalable and secure data infrastructure on Databricks, covering both streaming and batch workloads. - Data Pipeline Development: Design, develop and maintain data pipelines, primarily in Python and Spark, to automate ingestion and transformation across internal systems, external providers and on-chain sources. - Data Modelling: Design and maintain dimensional data models and transformation layers in dbt, with tests and documented contracts, so metrics are consistent and reusable across the company. - Data Lake Management: Oversee the data lake and lakehouse layers, ensuring efficient storage, effective partitioning, high data quality, and monitoring and alerting that surfaces issues early. - Integration and Customisation: Integrate Databricks with a wide range of data sources, including change data capture from operational databases, third-party APIs and blockchain data, and adapt data flows to specific business needs. - Performance, Scalability and Cost: Optimise pipelines and storage for performance, reliability and cost efficiency at scale. - Data Governance and Security: Apply best practices for governance, security and compliance in cloud and Databricks environments, including access control, encryption and monitoring. - Collaboration and Documentation: Work closely with platform engineers, data analysts and other stakeholders to understand data requirements, and document infrastructure, models and best practices. REQUIRED QUALIFICATIONS - Experience in Data Engineering: 3+ years as a Data Engineer, with hands-on ownership of production pipelines and lakehouse or data warehouse architecture. - Databricks and Spark: Strong practical experience with Databricks, Delta Lake and Spark, including both streaming and batch processing, and the judgement to choose between them. - Data Modelling: Solid understanding of dimensional modelling, slowly changing dimensions and data warehouse design, with the ability to define and defend the grain of the models you build. - Transformation Frameworks: Experience with dbt or an equivalent framework for managing transformations, testing and lineage. - Cloud Proficiency: Strong cloud fundamentals, including identity and access management, object storage, networking and infrastructure as code. We run on AWS; deep experience with Azure or GCP transfers well. - Proficiency in Python and SQL: Comfortable writing production transformations as well as services and connectors. - Data Quality and Governance: Experience implementing testing, monitoring and governance practices in cloud data environments. PREFERRED SKILLS - Experience with change data capture and replication from operational databases - Familiarity with blockchain or on-chain data - Infrastructure as code, ideally Terraform, and CI/CD for data workloads - Experience with BI and semantic layers such as Holistics, Looker or similar - Containerisation and orchestration (Docker, Kubernetes) ADDITIONAL INFORMATION This is a global and fully remote role. You must have the right to work for the country you are based. WHY WORK AT TRUST WALLET? - Be a part of the world’s leading blockchain ecosystem that continues to grow. - Excellent learning and career development opportunities. - Work alongside diverse, world-class talent, in an environment where learning and growth opportunities are endless. - Tackle fast-paced, challenging and unique projects. - Work in a truly global organization, with international teams and a flat organizational structure. - Work fully remotely with flexible working hours. - Enjoy competitive salary and benefits. Apply today to join our team in building the world’s most trusted and secure crypto wallet and enable a decentralized future for everyone. Trust Wallet is committed to being an equal opportunity employer. We believe that having a diverse workforce is fundamental to our success. *Due to the large amount of the applications, please consider your application as unsuccessful should you not be contacted within 4 weeks from your application date.

Location: Zurich, Switzerland Workplace: remote About the Project  Project Hydrus is a scripted and conversational audio data collection project. We're looking for native-level speakers who can deliver natural, everyday-sounding speech — recordings that hold up whether you're reading a script or speaking spontaneously.  Please note: the recordings you produce may be used to create synthesized, AI-generated voices for conversational AI products. Full details on how your data is collected, used, shared, and retained are set out in a Consent Form, which you'll review and sign before any paid session recording begins.  What You'll Do If Selected:      - Two supervised sessions of approximately 4 hours each, guided in real time by a Session Director on a separate recording platform.      - Record scripted material in a natural, conversational tone — faithfully, without a polished, performed, or “announcer” delivery.      - Record unscripted, spontaneous speech on a given topic, matching the same warmth and pace as your scripted takes.      - Set up and manage your own recording space and equipment for session recording.      - Deliver all recordings entirely in the required language (with specific dialect), without code-switching into other languages or dialects.  What We're Looking For  Required      - Experience as a voice actor, actor, or theater performer, or a comparable performance background.      - Native or native-equivalent fluency in the required language (with specific dialect).      - Access to a quiet recording space, free of echo and background noise (acoustically treated for session recording, if selected).      - Owns or will acquire a dedicated, professional-grade microphone for the audition (e.g. RODE NT-USB Mini, Blue Yeti), upgrading to a qualifying USB condenser or XLR mic + audio interface for session recording if selected. Built-in laptop mics, AirPods, earbuds, and headsets are not accepted at any stage.      - Owns closed-back monitoring headphones (required for session recording if selected).      - Comfortable both self-directing for the audition and taking live direction from a Session Director in guided sessions.  Preferred (nice to have, not required)      - Professional voice-over experience (dubbing/ADR, audiobook, podcasting) in a conversational rather than commercial/announcer register.      - Baseline audio hygiene: comfortable setting mic distance and catching noise, clipping, or distortion yourself.      - An existing home recording setup already treated for sound.  Your Data & Privacy  Before any session recording, you'll be asked to review and sign a Consent Form covering how your voice recordings are collected, used (including to train synthesized/AI voice models), shared with approved partners, retained, and how to exercise your data rights (access, correction, deletion, and more, depending on where you live). If you'd like to review these terms before applying, contact us and we'll share a copy in advance.  #L1-CC1

Location: Australia Workplace: remote About the Project  Project Hydrus is a scripted and conversational audio data collection project. We're looking for native-level speakers who can deliver natural, everyday-sounding speech — recordings that hold up whether you're reading a script or speaking spontaneously.  Please note: the recordings you produce may be used to create synthesized, AI-generated voices for conversational AI products. Full details on how your data is collected, used, shared, and retained are set out in a Consent Form, which you'll review and sign before any paid session recording begins.  What You'll Do If Selected:      - Two supervised sessions of approximately 4 hours each, guided in real time by a Session Director on a separate recording platform.      - Record scripted material in a natural, conversational tone — faithfully, without a polished, performed, or “announcer” delivery.      - Record unscripted, spontaneous speech on a given topic, matching the same warmth and pace as your scripted takes.      - Set up and manage your own recording space and equipment for session recording.      - Deliver all recordings entirely in the required language (with specific dialect), without code-switching into other languages or dialects.  What We're Looking For  Required      - Experience as a voice actor, actor, or theater performer, or a comparable performance background.      - Native or native-equivalent fluency in the required language (with specific dialect).      - Access to a quiet recording space, free of echo and background noise (acoustically treated for session recording, if selected).      - Owns or will acquire a dedicated, professional-grade microphone for the audition (e.g. RODE NT-USB Mini, Blue Yeti), upgrading to a qualifying USB condenser or XLR mic + audio interface for session recording if selected. Built-in laptop mics, AirPods, earbuds, and headsets are not accepted at any stage.      - Owns closed-back monitoring headphones (required for session recording if selected).      - Comfortable both self-directing for the audition and taking live direction from a Session Director in guided sessions.  Preferred (nice to have, not required)      - Professional voice-over experience (dubbing/ADR, audiobook, podcasting) in a conversational rather than commercial/announcer register.      - Baseline audio hygiene: comfortable setting mic distance and catching noise, clipping, or distortion yourself.      - An existing home recording setup already treated for sound.  Your Data & Privacy  Before any session recording, you'll be asked to review and sign a Consent Form covering how your voice recordings are collected, used (including to train synthesized/AI voice models), shared with approved partners, retained, and how to exercise your data rights (access, correction, deletion, and more, depending on where you live). If you'd like to review these terms before applying, contact us and we'll share a copy in advance. #L1-CC1

Location: United Kingdom; Europe; London Workplace: Remote WHO ARE WE? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! ABOUT NORTH: North https://cohere.com/north is Cohere's cutting-edge AI workspace platform, designed to revolutionize the way enterprises utilize AI. It offers a secure and customizable environment, allowing companies to deploy AI while maintaining control over sensitive data. North integrates seamlessly with existing workflows, providing a trusted platform that connects AI agents with workplace tools and applications. WHY THIS ROLE? This role offers a unique opportunity to shape how enterprises harness the power of AI in real-world applications. As a bridge between our core North product and our clients’ engineering teams, you’ll be at the forefront of solving complex problems and securely integrating AI into critical sectors such as finance, healthcare, and telecommunications. We’re looking for Software Engineers with Applied AI experience who can own the design, build, and deployment of agentic workflows powered by Large Language Models (LLMs), from early prototypes to production-grade AI agents, to deliver concrete business value in enterprise workflows. You’ll work closely with customers on real-world business problems, often building first-of-their-kind agent workflows that integrate LLMs with tools, APIs, and data sources. While our pace is startup-fast, the bar is enterprise-high: agents must be reliable, observable, safe, and auditable from day one. Location: UK Active DV clearance is highly preferred In this role, you will: - Work closely with our enterprise customers to translate high-value, ambiguous business problems into well-framed agentic workflows with clear success criteria and evaluation methodologies - Lead the design, build, and delivery of LLM-powered agents that reason, plan, and act across tools, APIs, and sensitive enterprise data sources, with enterprise-grade reliability and performance - Build and ship features for North, our AI workspace platform, working across the full product lifecycle from conceptualisation through production - Take ownership of scoping and shaping use cases end-to-end, flexing into whatever technical area the problem demands (including frontend) to drive the most effective solution - Contribute to shared frameworks and patterns that enable consistent, high-quality delivery across customers and teams - Drive clarity in ambiguous situations, build alignment, and raise engineering quality across the organization - Travel up to 20–40% to work on-site with customers and partners You may be a good fit if: - You have hands-on experience building and deploying production-grade software in Python; you write clean, testable, observable, scalable code - You've built and deployed highly performant RAG and agentic applications, including agents that plan and execute multi-step tasks using patterns like ReAct or Plan-and-Execute - You're deeply familiar with the LLM stack: frontier models, vector databases, and orchestration frameworks - You have a proven ability to build robust evaluation frameworks, moving well beyond trial and error, to measure agent accuracy, safety, and latency - You’re experienced working directly with customers and can lead technical discussions with enterprise stakeholders, translating ambiguous business needs into concrete technical specs - You have experience owning the full scope of a use case end-to-end - You thrive in fast-paced and ambiguous environments and can execute well even when priorities are shifting It's a bonus if you have: - Experience setting architectural standards for AI and agentic systems across distributed teams - Experience flexing into unfamiliar technical areas, such as frontend, when the problem calls for it - Exposure to regulated or sensitive industry environments (finance, healthcare, telecoms) - Experience with enterprise security, compliance, or auditability requirements for AI systems FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS: - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. - Full health and dental benefits, including a separate budget for mental health. - RRSP matching, 401K, Pension Scheme. - 100% Parental Leave top-up for up to 6 months, for either parent. - Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and a workspace improvement credit. Education & learning stipend for conferences, courses, and coaching. - 6 weeks of paid vacation (30 working days!) - Budget for traveling to other offices if you are remote, plus an annual company offsite. HOW AND WHERE WE WORK: - Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon. - For those in the office: a daily lunch program, plenty of snacks, and regular community and social events. - For those not near an office: a co-working benefit so you can work alongside others in your city. - Everyone receives a $500 home office stipend to set up your workspace properly. If any of the above doesn’t line up exactly with your experience, we still encourage you to apply. We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs. We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider. Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.

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

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

Задачи: - Поддерживать текущие алгоритмические решения - Проектировать и внедрять AI-агентов (LLM, GPT-like модели) в различные процессы - Адаптировать и дообучать LLM (например, LLaMA, Mistral, YaLM 2) под специфику продукта - Разрабатывать RAG-пайплайн для поиска и обработки данных - Настраивать векторные БД (Pinecone/Weaviate/Qdrant) и оптимизировать кэширование Требования: - Знание современных AI-фреймворков и библиотек (LangChain, LlamaIndex и других) - Уверенное владение Python и PyTorch/TensorFlow - Практический опыт работы с API, Docker/Kubernetes и базами данных - Опыт внедрения RAG

Location: Philippines; India; Mexico City, Mexico; Mexico; Cairo, Egypt; Brazil; Malaysia; Indonesia Workplace: remote About the Role  We are looking for a detail-oriented QC Reviewer to ensure the quality and consistency of robot manipulation video annotations produced by our annotation team. You'll review labeled video segments against a detailed style guide and reference examples, catch errors or inconsistencies, and provide clear, constructive feedback to annotators. This role is critical to maintaining the accuracy and reliability of training data used for robotics AI models.  What You'll Do      - Review completed annotations of robot manipulation videos across three levels of labeling detail (atomic motion, skill/subtask, task/goal).      - Cross-check annotator descriptions against all three camera views (overhead and both wrist-mounted cameras) to verify accuracy of described actions, object identification, and contact/grasp details.      - Verify full coverage of each video — confirm there are no gaps in labeling, including idle or pause moments.      - Check that descriptions accurately reflect what actually happened in the video, including failures, drops, or slipped grips — flagging any instances where outcomes were inaccurately described or "smoothed over."      - Evaluate annotations for adherence to the style guide: correct vocabulary usage, spatial/directional accuracy, consistent terminology for objects and actions, and appropriate language variation (avoiding repetitive phrasing).      - Identify recurring errors or patterns of confusion among annotators and report them to the team lead for calibration purposes.      - Provide specific, actionable feedback on rejected or revised annotations, referencing the style guide and reference examples.      - Participate in periodic calibration sessions with the team and client to align QC standards with evolving guidelines or edge-case resolutions.      - Maintain a QC log or tracking sheet documenting review outcomes, common error types, and annotator performance trends (as required by the project).    What We're Looking For  Required:      - Excellent written English and strong reading comprehension — you'll be evaluating precise, nuanced language choices, not just checking for typos.      - Exceptional attention to detail — able to catch subtle mismatches between video content and written description (e.g., a mislabeled grasp type, an incorrect object part, a missed failure event).      - Strong grasp of spatial/mechanical description (left/right, above/below, object parts like handles, lids, edges) to verify annotator accuracy.      - Ability to apply a detailed, structured style guide consistently and make sound judgment calls in ambiguous or edge-case scenarios.      - Comfort giving clear, specific, and constructive feedback to annotators (written feedback, not just pass/fail marks).      - Reliable, self-directed work habits, with the ability to work independently while meeting review turnaround expectations.  Nice to Have:      - Prior experience in a QC, editing, proofreading, or annotation review role.      - Experience with video annotation, data labeling, or transcription QA specifically.      - Familiarity with robotics terminology (grippers, end-effectors, manipulation).      - Experience with annotation tools such as Label Studio, including review/approval workflows.  #L1-CC1 Why Join Welo Data?   ✨ Limitless Flexibility   Project-based opportunities that fit your availability. Choose when and how much you want to contribute—fully remote, with complete autonomy.    🌱 Limitless Growth   Optional access to AI and Large Language Model workshops designed specifically for professionals like you. No coding required—just your expertise.    🌍 Limitless Support   Be part of a global contributor community with responsive guidance and support.    💡 Real Impact   Apply your expertise in the Legal field to influence the AI systems shaping the future of your industry—while collaborating with data professionals and expanding your skills.     How to Apply?  Apply now by answering a few quick questions to join our database and become part of our growing community.    About Welo Data  Welo Data, part of Welocalize, is a global AI data company with 500,000+ contributors delivering high-quality, ethical data to train the world’s most advanced AI systems. We’re building smarter, more human AI with a diverse community in 100+ countries.   At Welo Data, Limitless AI. Limitless You. isn’t just a slogan—it’s our promise. We build smarter AI through the power of human contribution, offering limitless opportunities for our global community to grow, contribute, and work on their terms.