{"id":2144777,"url":"https://alion.io/job/yurt-tech-lead-computer-vision-engineer-computer-vision-tech-lead","title":"Lead Computer Vision Engineer / Computer Vision Tech Lead","company":{"id":94099,"name":"Yurt Tech","domain":"yurt.group","url":"https://alion.io/company/yurt-tech","size_band":null,"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":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Astana, Kazakhstan"],"countries":["KZ"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":700000,"max":1700000,"currency":"KZT","period":"month","gross":true,"usd_annual":45636},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Computer Vision","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"Docker","optional":false},{"name":"FastAPI","optional":false},{"name":"gRPC","optional":false},{"name":"GStreamer","optional":false},{"name":"Kubernetes","optional":false},{"name":"Multimodal AI","optional":false},{"name":"ONNX","optional":false},{"name":"OpenCV","optional":false},{"name":"OpenVINO","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"Quantization","optional":false},{"name":"TensorRT","optional":false},{"name":"Transformers","optional":false},{"name":"Triton Inference Server","optional":false},{"name":"YOLO","optional":false},{"name":"Apache Kafka","optional":true},{"name":"C++","optional":true},{"name":"Function Calling","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"LLM","optional":true},{"name":"MLFlow","optional":true},{"name":"RAG","optional":true},{"name":"vLLM","optional":true},{"name":"VLM","optional":true}],"status":"live","first_seen_at":"2026-09-17T05:49:40Z","employer_posted_date":"2026-09-17","last_verified_at":"2026-10-10T09:09:21Z","board_verified":false,"closed_at":null,"days_open":25,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":25},"description":"Мы разрабатываем масштабную систему видеомониторинга и интеллектуальной видеоаналитики для государственного проекта.\n Ищем технического лидера направления Computer Vision, который возьмет на себя архитектуру AI-решений, будет участвовать в разработке ключевых компонентов и развивать инженерную команду.\n Основные задачи\n Техническое руководство командой Computer Vision Engineers.\n Менторинг разработчиков, проведение code review и развитие инженерных практик.\n Декомпозиция задач, оценка сроков и распределение технических задач внутри команды.\n Проектирование архитектуры production-систем видеоаналитики.\n Принятие ключевых технических решений и контроль качества реализации.\n Разработка и оптимизация пайплайнов обработки десятков и сотен RTSP-видеопотоков с минимальной задержкой.\n Работа с NVIDIA DeepStream, GStreamer, TensorRT и Triton Inference Server.\n Выбор, разработка и внедрение моделей Computer Vision: YOLO, ViT, DETR, Swin и Video Transformers.\n Оптимизация инференса на GPU и CPU с использованием ONNX, TensorRT, OpenVINO, CUDA и Quantization.\n Организация разработки AI-сервисов на FastAPI или gRPC с применением Docker и Kubernetes.\n Участие в найме и технической оценке новых специалистов.\n Взаимодействие с архитекторами, backend-командой, DevOps и представителями бизнеса.\n Исследование и внедрение новых подходов в области Video Analytics и Multimodal AI.\n Требования\n Сильный коммерческий опыт в Computer Vision и Video Analytics.\n Опыт технического лидерства или наставничества инженерной команды.\n Практический опыт проектирования и запуска production AI-систем.\n Уверенное знание Python, PyTorch, OpenCV и современных моделей семейства YOLO.\n Практический опыт работы с RTSP, GStreamer, NVIDIA DeepStream и TensorRT.\n Опыт оптимизации моделей с использованием ONNX, TensorRT, OpenVINO, CUDA или Quantization.\n Понимание архитектуры высоконагруженных систем обработки видеопотоков.\n Знание принципов работы современных Vision Transformers и мультимодальных моделей.\n Опыт работы с Docker и Kubernetes.\n Умение принимать архитектурные решения, аргументировать их и доносить до команды.\n Навыки постановки задач, code review и развития инженеров.\n Будет преимуществом\n Опыт работы с Triton Inference Server.\n Опыт разработки на C++ и CUDA.\n Знание Kafka и систем потоковой обработки данных.\n Опыт применения MLflow и построения MLOps-процессов.\n Опыт интеграции Computer Vision с LLM и мультимодальными моделями.\n Знакомство с VLM, RAG, LangGraph, vLLM и Function Calling.\n Опыт участия в найме и формировании AI-команд.\n Мы предлагаем\n Техническое лидерство в масштабном государственном проекте.\n Возможность формировать архитектуру и технологический стек решения.\n Сильную инженерную команду и возможность влиять на ее развитие.\n Современный стек: Computer Vision, Video Analytics, GPU-инференс и Multimodal AI.\n Участие в создании высоконагруженной системы промышленного уровня.\n Конкурентную заработную плату.\n Полную занятость и работу в офисе в 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