{"id":3676522,"url":"https://alion.io/job/wbtech-python-backend-ai-engineer","title":"Python Backend / AI Engineer","company":{"id":4504131,"name":"WB-Tech","domain":"wbtech.ru","url":"https://alion.io/company/wbtech","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":null,"employment_type":null,"work_mode":"remote","remote_scope":"stated_regions","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Moscow, Russia"],"countries":["RU"],"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":"Asyncio","optional":false},{"name":"Django","optional":false},{"name":"Docker","optional":false},{"name":"Docker Compose","optional":false},{"name":"FastAPI","optional":false},{"name":"Function Calling","optional":false},{"name":"Git","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"Linux","optional":false},{"name":"LLM","optional":false},{"name":"OpenAI Agents SDK","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"Redis","optional":false},{"name":"Rest API","optional":false},{"name":"CI/CD","optional":true},{"name":"Gemma","optional":true},{"name":"Llama","optional":true},{"name":"llama.cpp","optional":true},{"name":"Ollama","optional":true},{"name":"Qwen","optional":true},{"name":"RAG","optional":true},{"name":"RunPod","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-11-06T11:17:08Z","employer_posted_date":null,"last_verified_at":"2026-11-06T11:17:08Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":0},"description":"Ищем самостоятельного разработчика, способного работать на пересечении классического Python Backend и AI Engineering.\nВажно, чтобы кандидат не ограничивался использованием готовых AI-библиотек, а понимал архитектуру агентных систем, умел самостоятельно проектировать решения, интегрировать модели в продукты и при необходимости заниматься их развертыванием и сопровождением.\n Основные обязанности\n Разработка и поддержка backend-сервисов на Python с использованием Django и FastAPI.\n Проектирование API, интеграций и архитектуры backend-приложений.\n Работа с PostgreSQL, Redis, оптимизация запросов и производительности сервисов.\n Разработка AI-агентов и агентных систем на базе LLM.\n Создание агентных решений как с использованием фреймворков (LangGraph, LangChain, OpenAI Agents SDK и аналоги), так и без них.\n Интеграция локальных и облачных LLM в существующие продукты.\n Разработка инструментов (tools), управление вызовами функций, состоянием и контекстом агентов.\n Контейнеризация, развертывание и сопровождение сервисов с использованием Docker.\n Работа с AI-инфраструктурой: запуск, настройка и поддержка LLM на собственных серверах и облачных GPU-платформах.\n Обязательные требования\n Уверенное знание Python, опыт коммерческой backend-разработки.\n Опыт работы с Django и FastAPI.\n Хорошее знание PostgreSQL и Redis.\n Уверенное владение Docker и Docker Compose.\n Понимание асинхронного программирования в Python (asyncio).\n Практический опыт интеграции LLM в приложения.\n Опыт разработки AI-агентов, использования tool calling / function calling.\n Понимание принципов работы LLM, управления контекстом и построения агентных процессов.\n Опыт работы с REST API, внешними сервисами и сторонними интеграциями.\n Уверенное владение Git, Linux.\n Будет большим плюсом\n Опыт работы с LangGraph, LangChain, OpenAI Agents SDK.\n Опыт самостоятельного проектирования агентных архитектур без использования готовых фреймворков.\n Опыт развертывания и обслуживания локальных LLM (Qwen, Gemma, Llama и другие).\n Опыт работы с GPU-инфраструктурой, RunPod и аналогичными платформами.\n Знание vLLM, llama.cpp, Ollama и других инструментов инференса.\n Понимание принципов квантования моделей, ограничений VRAM и оптимизации инференса.\n Опыт построения RAG-систем, работы с эмбеддингами и векторным поиском.\n Опыт настройки мониторинга, логирования и 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