MLOps Engineer

Main mission

Manages the entire lifecycle of models, from deployment to monitoring.

5 key responsibilities

• Build continuous deployment pipelines for models (CI/CD).
• Automate training, testing, and updating of models.
• Monitor performance, drift, and costs of models in production.
• Manage computing infrastructure (GPU, cloud, containers).
• Define standards for versioning, reproducibility, and rollback.

Key skills

Kubernetes, Docker, MLflow, Airflow, CI/CD pipelines, cloud, observability.

What's expected

Zero 'orphan' models in production. Reproducible, monitored systems with controlled costs.

Career paths

AI Platform Architect, Head of ML Engineering, AI Solutions Architect.