ML-инженер вакансии

Languages required: English (excellent communication skills), Japanese (not required, but useful).

Location: Yerevan; Belgrade; Novi Sad, Vojvodina; Poland; Kyiv City; Odesa 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 senior pod alongside an FDE, an FDX, and Senior AI Engineers — contributing to real delivery work while building toward independent ownership. Requirements: Mindset Proactive and self-directed; you push for clarity rather than waiting for a ticket. Excellent communication and problem-solving skills. Comfortable with some ambiguity, with support from senior team members as you take on more. B2+ English, comfortable collaborating across distributed, multicultural teams. Technical depth Hands-on experience building or contributing to RAG systems, ideally in a production or near-production setting. Solid engineering fundamentals; Python and/or TypeScript proficiency. Productive in an unfamiliar codebase with some ramp-up support. Practical AWS experience (Lambda, S3, ECS, or similar); ready to grow into Bedrock and Bedrock AgentCore . Some experience with containers and CI/CD in real projects. Exposure to evaluating non-deterministic systems — you've contributed to or run test/eval cycles, even if you haven't owned a full eval suite end-to-end. Basic working knowledge of model/agent monitoring concepts. Awareness of cost and latency trade-offs when working with LLMs. Some hands-on exposure to the Claude ecosystem (Claude Code, CLAUDE.md, hooks, skills files) is a plus, or strong ability to ramp up quickly. Practical experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) in real projects. 2+ years of software or ML engineering experience, including some exposure to production systems. Solid AI/ML foundations — you understand what the models do well enough to reason about common failure modes. 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 (or actively pursuing them). A2A: Interest in agent-to-agent interoperability concepts. 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. 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. MCP: you can say why an agent would prefer it to a REST integration, having authored a server is an additional  plus. Responsibilities: Build and contribute to RAG system components under senior guidance, with growing autonomy. Write tests and help build out evaluation harnesses for the features you work on. Write production code across the stack (AI, backend services, data pipelines) with code review support. Help integrate AI components into backend services and RESTful APIs. Support deployment of systems to AWS (containerized, CI/CD), taking on more of this independently over time. Contribute to documentation, runbooks, and client handover materials. Participate in technical discussions and architectural decisions, with an eye toward taking on more of this independently. Support model evaluation efforts and help investigate and improve failure modes. Take on increasing ownership of components and technical decisions as you grow in the role. 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

Senior Data Engineer (ML-направление) в Ozon Банк — онлайн-банк группы Ozon, предоставляющий финансовые услуги и сервисы для покупателей и продавцов маркетплейса.

Сейчас исследуем и внедряем LLM для решения бизнес-задач внутри компании, улучшения пользовательского опыта и автоматизации процессов.

Требуется ML-инженер в компанию SimbirSoft. Требуемые навыки: — Python

Мы ищем ML-разработчика для создания и развития моделей машинного обучения в продуктах информационной безопасности.

Работа на месте в Базеле, Швейцария. Подробное описание вакансии доступно на LinkedIn по ссылке: https://www.linkedin.com/jobs/view/4461292600/.

Задачи: • Владение архитектурой и поставкой сервисов прогнозирования, ранжирования и рекомендаций. • Проектирование воспроизводимых рабочих процессов обучения, оценки, мониторинга и переобучения. • Перевод целей продукта в измеримые задачи машинного обучения, эксперименты и релизы. Требования: • Более 5 лет профессионального опыта в разработке программного обеспечения или машинного обучения с производственными системами. • Продвинутые знания Python и SQL; сильные навыки в статистике, экспериментах и оценке моделей. • Опыт работы с оркестрацией, контейнерами, облачной инфраструктурой, наблюдаемостью и CI/CD. • Умение отлаживать ошибки данных и моделей. • Опыт в прогнозировании, ранжировании, рекомендациях, оптимизации или другой прикладной области машинного обучения. • Владение английским языком для технической документации и межфункционального взаимодействия.

Ipotekabank — банк в Узбекистане, входящий в международную финансовую OTP Group, специализирующийся на ипотечном и розничном кредитовании. Вакансии: - Principal QA Engineer (AI Engineering) - Senior QA Engineer - Senior QA специалист Офис в Ташкенте.

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)

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: Foster City, CA Workplace: Hybrid Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation. About the Role Join Replit's key teams across the company, such as AI Research, Strategic Finance, or the Office of the CEO, for a unique paid internship built for sharp quantitative and creative minds. You will work alongside our top executives, in addition to world-class engineers, designers, and finance team on some of the hardest problems in AI-native software creation and accelerating key areas of our business. We are creating a dedicated track for students with strong mathematical backgrounds because the problems we are solving sit at the intersection of deep math and applied AI, including agent reasoning, systems optimization, and improving how our models learn and perform at scale. Your work will directly shape how millions of users build software. You Will: - Contribute to real engineering problems that push the boundaries of AI-powered software creation - Collaborate with engineers, designers, and product managers on infrastructure that powers Replit's platform - Prototype novel approaches to problems in AI, systems, or tooling where mathematical rigor is the differentiator - Ship work that impacts millions of developers globally, in an environment where your ideas are heard and often implemented Required Skills and Experience: - Currently pursuing a Bachelor's, Master's, or PhD in Mathematics, Computer Science, Computer Engineering, Statistics, Physics, or a related quantitative field - At least one semester of schooling remaining after the internship - Demonstrated excellence in competitive mathematics such as IMO and IOI, quantitative research, or advanced coursework - Genuine curiosity about AI, agent systems, company building, or developer tooling - Extremely bullish on Replit and the future of AI-native software creation. We would love to see what you have built on the platform What We Value: - First-principles thinking: Ability to break down complex problems from the ground up and reason about them rigorously - Mathematical intuition: Strong grasp of the underlying math behind machine learning, optimization, or systems - Self-directed and autonomous: Capable of working independently while collaborating well with cross-functional teams - Strong communication: Ability to explain complex quantitative concepts to both technical and non-technical audiences - Curiosity and speed: Genuine excitement for hard problems and the drive to iterate quickly Full-Time Employee Benefits Include: 💰 Competitive Salary & Equity 💹 401(k) Program with a 4% match (US Only) ⚕️ Health, Dental, Vision and Life Insurance 🩼 Short Term and Long Term Disability 🚼 Paid Parental, Medical, Caregiver Leave 🏝 Flexible Time Off (FTO) + Holidays 🚗 Commuter Benefits (In-Office & US Only) 📱 Monthly Wellness Stipend 🧑‍💻 Autonomous Work Environment 🖥 In Office Set-Up Reimbursement (In-Office Only) 🚀 Quarterly Team Gatherings ☕ In Office Amenities (In-Office Only) Want to learn more about what we are up to? - Self-driving Company https://replit.com/blog/self-driving-company - Replit Agent at Scale https://replit.com/blog/evaluating-and-improving-agent-at-scale - AI Adoption https://replit.com/blog/ai-adoption - Build Open-Source Apps https://replit.com/build/open-source-app-builder   Interviewing + Culture at Replit - Operating Principles https://blog.replit.com/operating-principles - Reasons not to work at Replit https://blog.replit.com/reasons-not-to-join-replit To achieve our mission of making programming more accessible around the world, we need our team to be representative of the world. We welcome your unique perspective and experiences in shaping this product. We encourage people from all kinds of backgrounds to apply, including and especially candidates from underrepresented and non-traditional backgrounds.

Задачи: — Проектировать и реализовывать end-to-end LLM-пайплайны, включая продвинутые RAG-системы и автономных AI-агентов (планирование, function calling, мультиагентная оркестрация) — Дообучение и адаптация: SFT/RLHF/DPO, LoRA/QLoRA под бизнес-задачи; решать, когда нужно дообучение, а когда достаточно промпт-инжиниринга или RAG — Поддерживать культуру качества: внедрять продуктовые и ML-метрики (RAGAS, DeepEval, LLM-as-a-Judge), автоматизировать регрессионное тестирование — Писать чистый, документированный и тестируемый python-код, проектировать API-контракты для ML-компонент — Участвовать в код-ревью, формировать технические стандарты команды, работать напрямую с продуктом и стейкхолдерами Требования: — Опыт в DL/NLP от 4 лет, включая обучение и дообучение моделей на PyTorch, из них не менее года практического опыта с LLM — Практический опыт построения AI-агентов и LLM-пайплайнов с использованием LangChain/LangGraph — Глубокое понимание архитектуры современных LLM: attention, MoE, RoPE, KV-cache, методы alignment (RLHF/DPO), принципы эффективного инференса — Сильная инженерная культура: Python (asyncio, pydantic, fastapi), чистый тестируемый код (pytest), асинхронные микросервисы; понимание Docker/K8s, CI/CD для ML — Оценка и мониторинг: уверенное владение инструментами LLMOps (Arize, LangSmith, W&B, Phoenix), опыт создания кастомных eval-пайплайнов — Работа с данными: опыт предобработки сложных данных для датасетов, знание SQL

Задачи: — Присоединиться к исследовательской группе в Университете Северного Техаса с осени 2027 года. — Участвовать в исследованиях в области искусственного интеллекта и машинного обучения, включая федеративное обучение, конфиденциальность, безопасность ИИ и большие языковые модели. Требования: — Сильная подготовка в области компьютерных наук, науки о данных или смежных областях. — Хорошие навыки программирования и аналитические способности. — Предпочтителен опыт научных исследований. Условия: — Начало обучения и работы с осени 2027 года. Контакт для отклика: Pretomroy.ovi@unt.edu