Senior MLOps Engineer

Operationalizes ML models within the stack using GitLab CI/CD, Docker, Kubernetes, and Airflow for retraining pipelines, alongside production model monitoring and drift detection.

• Build and maintain CI/CD pipelines specifically for ML model deployment, versioning, and rollback (GitLab CI).
• Containerize ML models and manage deployment via Kubernetes, ensuring scalable and reliable serving infrastructure.
• Design and operate Airflow-based retraining pipelines, scheduling and monitoring model refresh cycles.
• Implement production model monitoring, performance metrics, latency, and resource usage.
• Build drift detection mechanisms to flag when models need retraining or investigation.
• Maintain model registries and versioning to ensure traceability across model lifecycle stages.
• Partner with the AI/ML Engineer and AI Solution Architect to standardize the path from prototype to production.
• Coordinate with the Senior DataOps role to align infrastructure patterns across data and ML workloads where they overlap.

• 5 - 6 years in DevOps/Platform Engineering, with 3+ years specifically focused on MLOps (model lifecycle automation, not general infra work).
• Strong hands-on Kubernetes and Docker experience for serving and scaling ML workloads specifically.
• Proven experience building GitLab CI/CD pipelines for ML model deployment and versioning.
• Experience operating Airflow for retraining pipeline orchestration (distinct from general data pipeline orchestration).
• Practical experience implementing model monitoring and drift detection in a production environment.
• Solid Python skills for tooling, automation, and integration with ML frameworks.