{"id":144028,"url":"https://alion.io/job/jetlyn-machine-learning-engineer","title":"Machine learning Engineer","company":{"id":771,"name":"Jetlyn","domain":"jetlyn.ru","url":"https://alion.io/company/jetlyn","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"stated_regions","remote_scope_basis":"inferred_company_offices","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["RU","BY","KZ","KG","AM"],"hiring_countries_total":5,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Airflow","optional":false},{"name":"Apache Kafka","optional":false},{"name":"CatBoost","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"Git","optional":false},{"name":"Grafana","optional":false},{"name":"Hugging Face","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"KServe","optional":false},{"name":"Kubernetes","optional":false},{"name":"LightGBM","optional":false},{"name":"LLM","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"NLP","optional":false},{"name":"NumPy","optional":false},{"name":"PEFT","optional":false},{"name":"PoC Library","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Redis","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"XGBoost","optional":false},{"name":"Transformers","optional":true}],"status":"live","first_seen_at":"2026-08-28T00:00:00Z","employer_posted_date":null,"last_verified_at":"2026-08-28T00:00:00Z","board_verified":false,"closed_at":null,"days_open":49,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":49},"description":"Навыки: Kubernetes, Apache Airflow, Redis, Prometheus, LLM, NLP, A/B тестирование, Python. Специализации: ML разработчик.\nОбязанности:\nРазработка ИИ ассистентов, функциональных агентов в рамках проверки PoC для ИИ идей. входящих в ИИ инкубатор:\n- практические познания в NLP,\n- запуск на гпуцпу\n- контенейризация\n- jenkins\n- векторные базы и эмбедеры для RAG\n- function calling\n- kserve\n- bert образные модели\n- работа с эвристиками\n- опыт в квантинизации ллм\nТребования:\nПонимание принципов проектирования AI-агентов: управление состоянием и контекстом, выбор инструментов, контроль уровня автономности и human-in-the-loop.\nГлубокое понимание основных принципов машинного обучения: подготовка данных и признаков, выбор моделей, регуляризация, переобучение, data leakage, интерпретация результатов.\nЗнание математической статистики: проверка гипотез, доверительные интервалы, дизайн и анализ A/B-тестов.\nПонимание принципов оценки качества ML-систем: offline- и online-метрики, построение валидации, оценка бизнес-эффекта, мониторинг качества моделей и данных.\nОпыт работы хотя бы в одном из направлений:\no классическое машинное обучение;\no временные ряды и прогнозирование;\no рекомендательные системы, поиск и ранжирование;\no NLP, LLM и генеративный AI.\nУверенное владение Python и основными библиотеками для анализа данных и машинного обучения: pandas или polars, NumPy, scikit-learn, CatBoost / LightGBM / XGBoost, PyTorch или аналогичные инструменты.\nУмение писать поддерживаемый production-код: модульная архитектура, тестирование, логирование, документация, работа с Git и участие в code review.\nУверенное владение SQL\nОпыт построения evals для LLM-систем, оценки качества ответов, контроля галлюцинаций и мониторинга поведения системы.\nПонимание рисков prompt injection, некорректного tool calling, утечки данных и чрезмерных прав доступа агента.\nОпыт работы с API языковых моделей, Hugging Face и одним из orchestration-фреймворков.\nУмение оценивать стоимость, latency и масштабируемость LLM-решений.\nОпыт работы в e-commerce, retail или других продуктовых highload-системах.\nОпыт работы с Airflow, MLflow, Kafka, Redis, Kubernetes, Prometheus и Grafana.\nОпыт эксплуатации GPU-инфраструктуры и оптимизации inference.\nОпыт fine-tuning или адаптации LLM, включая LoRA / PEFT.\nОпыт проектирования agentic workflows или multi-agent-систем.\nПонимание принципов информационной безопасности, privacy и работы с персональными данными.Обязательные требования• Владение Python и основными библиотеками для анализа данных и машинного обучения: pandas или polars, NumPy, scikit-learn, CatBoost / LightGBM / XGBoost, PyTorch или аналогичные инструменты.\nВладение SQL.\nОпыт работы в e-commerce, retail или других продуктовых highload-системах.\nОпыт работы с Airflow, MLflow, Kafka, Redis, Kubernetes, Prometheus и 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