Machine Learning Infrastructure Specialist (MLOps Engineer)

Design, implementation, and support of end-to-end ML pipelines: from data preparation to model deployment and monitoring in the SOC production environment. Automation of the full ML model lifecycle: data versioning (LakeFS), experiment tracking (MLflow), packaging, and deployment (Docker/K8s). Development and support of infrastructure based on Docker and Kubernetes (K8s) for R&D and production environments. Organization of the workspace and MLOps support for laboratory data scientists to accelerate research and development cycles. Implementation of practices for monitoring model performance, data drift, and infrastructure metrics (Prometheus, Grafana). Ensuring the security of MLOps processes (MLSecOps): secrets management (Vault), image scanning (Harbor), and dependency control.

Requirements

Higher technical education in the field of information security. At least 3 years of experience in an MLOps engineer role. Confident experience working with Docker and Kubernetes (K8s) in a production environment. Practical experience in building ML pipelines using Airflow and MLflow. Understanding of CI/CD principles and experience applying them to ML systems. Experience working with object storage (S3-compatible, e.g., MinIO). Proficiency in SQL at a level sufficient for working with analytical databases (ClickHouse). Familiarity with core ML libraries (Scikit-learn, PyTorch/TensorFlow) at a level sufficient for packaging and deploying models developed by data scientists. Technical English.

Higher education — specialist degree, master's degree; Experience: 3 years