{"id":2431068,"url":"https://alion.io/job/ylab-development-ml-engineer","title":"ML Engineer","company":{"id":1040,"name":"YLAB Development","domain":"ylab.io","url":"https://alion.io/company/ylab-development","size_band":"1-10","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"HeadHunter","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"remote","remote_scope":"stated_regions","remote_scope_basis":"inferred_board_market","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Moscow, 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Systems","optional":true},{"name":"PEFT","optional":true},{"name":"pgvector","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Prometheus","optional":true},{"name":"Prompt Engineering","optional":true},{"name":"RAG","optional":true},{"name":"Ragas","optional":true},{"name":"Vertex AI","optional":true}],"status":"live","first_seen_at":"2026-10-13T09:05:40Z","employer_posted_date":"2026-10-13","last_verified_at":"2026-10-13T20:25:12Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Ищем ML-инженера, который умеет не просто обучать модели, а доводить их до production: от данных и feature engineering до деплоя, мониторинга и улучшения качества в реальных системах.\n Что будет в работе\n Разработка и развитие ML-моделей: классические ML (классификация, регрессия, ранжирование), deep learning, NLP.\n\n Проектирование и реализация end-to-end ML-пайплайнов: от данных и feature engineering до обучения, валидации и деплоя.\n\n Работа с данными: ETL/ELT, подготовка датасетов, feature store, работа с большими объёмами данных.\n\n Деплой моделей в production: Docker, Kubernetes, REST/gRPC API, batch/online inference.\n\n Настройка MLOps: MLflow/W&B, версионирование моделей и данных, CI/CD/CT, автоматизация переобучения.\n\n Мониторинг моделей в production: метрики качества, latency, throughput, data/model drift, алертинг.\n\n Участие в архитектурных решениях: выбор подходов, оценка trade-off’ов, влияние на стабильность и масштабируемость системы.\n\n Взаимодействие с data scientists, инженерами, аналитиками и бизнес-стейкхолдерами.\n\n Что важно\n Коммерческий опыт в ML/ML Engineering от 3 лет, уровень Middle+ / Senior.\n\n Уверенное владение Python:pandas, NumPy, scikit-learn, PyTorch или TensorFlow.\n\n Опыт построения и деплоя ML-моделей в production (не только Jupyter/прототипы).\n\n Понимание классического ML: supervised learning, feature engineering, валидация, метрики качества.\n\n Опыт работы с данными: SQL, ETL/ELT, Spark/Databricks (желательно), работа с большими датасетами.\n\n Опыт работы с Docker, базовое понимание Kubernetes, CI/CD (GitLab CI, GitHub Actions).\n\n Опыт настройки MLOps: MLflow/W&B, версионирование моделей/данных, автоматизация пайплайнов (Airflow, Kubeflow, Databricks Workflows).\n\n Понимание мониторинга моделей в production: метрики, drift detection, алертинг, retraining.\n\n Умение работать автономно, брать задачу целиком и доводить до результата.\n\n Умение переводить бизнес-требования в технические решения и аргументировать свой выбор.\n\n Будет плюсом\n Опыт работы с LLM/GenAI: RAG, embeddings, vector databases (FAISS, Milvus, pgvector), prompt engineering, fine-tuning (LoRA)\n\n Практика LLMOps: MLflow, Langfuse, LangSmith, Weights & Biases, evaluation harnesses (Ragas, DeepEval)\n\n Опыт работы с agentic systems: LangGraph, CrewAI, multi-agent workflows\n\n Опыт работы с облачными ML-платформами: AWS SageMaker, Azure ML, Vertex AI\n\n Опыт работы с feature stores (Feast, Tecton), model registries, A/B testing, canary deployments\n\n Знание паттернов MLOps: CI/CD/CT, drift detection, rollback, monitoring (Prometheus, Grafana, Evidently, Arize)\n\n Английский язык для работы с зарубежной документацией, командами и стейкхолдерами.\n\n Английский\n Для проектов без английского язык не требуется.\n\n Для европейских и американских проектов нужен английский от B2+.\n\n Процесс\n Короткое общение с рекрутером.\n\n Техническое интервью / TechCertify-скрининг.\n\n Передача на подходящие проекты клиентам.\n\n Если вам близок инженерный подход к ML, нравится не просто «обучать модели», а доводить их до production и влиять на реальные продукты - будем рады 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