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инженерии и от 2-3 лет в роли MLOps Engineer\n Уверенный опыт работы с Kubernetes, Helm, Terraform и Ansible\n Опыт построения и эксплуатации MLOps-пайплайнов, CI/CD и GitOps-процессов для ML-продуктов\n Понимание ML-инженерии: distributed training, особенности production-инференса, оптимизация и квантование моделей\n Опыт проектирования распределенных систем и работы с интеграционными паттернами: микросервисы, очереди сообщений, gRPC\n Опыт настройки мониторинга и наблюдаемости: Prometheus, Grafana, Evidently AI или аналоги\n Уверенное владение Python и Bash для автоматизации и работы с инфраструктурой\n Практика работы с хранилищами и данными: S3-совместимые хранилища, ClickHouse, PostgreSQL, Feature Store\n Умение аргументированно защищать архитектурные решения, взаимодействовать с Data Scientists и оценивать инфраструктурные затраты\n Готовность брать техническое лидерство: проводить ревью, развивать инженерные стандарты и менторить 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