{"id":1168464,"url":"https://alion.io/job/alabuga-razrabotchik-llm-3","title":"Разработчик LLM","company":{"id":354,"name":"Alabuga","domain":"alabuga.ru","url":"https://alion.io/company/alabuga","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"HeadHunter","truth_index":{"grade":"B","score":81,"open_postings":112,"ghost_share":0.25,"stale_share":0,"repost_share":0.571,"time_to_fill_p50_days":6,"computed_at":"2026-10-13T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Krasnodar, Russia"],"countries":["RU"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":127500,"max":null,"currency":"RUB","period":"month","gross":false,"usd_annual":17508},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"Hugging Face","optional":false},{"name":"LLM","optional":false},{"name":"LoRA","optional":false},{"name":"PEFT","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"SFT","optional":false},{"name":"Tool Use","optional":false},{"name":"Transformers","optional":false}],"status":"live","first_seen_at":"2026-09-24T05:24:02Z","employer_posted_date":"2026-09-24","last_verified_at":"2026-10-13T09:32:55Z","board_verified":true,"closed_at":null,"days_open":19,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":19},"description":"Обязанности: \n проектирование архитектуры LLM и выбор базовой модели под задачи проекта;\n создание и обучение моделей с нуля либо проведение fine-tuning (SFT, LoRA, PEFT);\n подготовка, очистка и разметка датасетов, формирование instruction-данных;\n реализация и запуск пайплайна обучения на базе PyTorch;\n оптимизация моделей (quantization, снижение latency, inference-оптимизация);\n реализация function/tool calling и интеграция LLM с API.\n Требования: уверенное владение PyTorch и опыт реализации пайплайнов обучения.\n понимание архитектуры Transformer (attention, embeddings, базовые механизмы работы моделей).\n опыт проектирования и применения методов дообучения (SFT, LoRA, PEFT).\n практический опыт оптимизации моделей (quantization, inference-оптимизация, снижение задержек);\n опыт работы с Hugging Face Transformers.\n Условия: релокация в Республику Татарстан;\n конкурентный уровень з/п;\n дневная рабочая неделя (ненормированная);\n транспорт до работы из г. Елабуга, г. Набережные Челны, г. Менделеевск, г. 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