ML OPS engineer

Role & responsibilities

• Deployment patterns: batch scoring, real-time endpoints, streaming where needed
• MLOps toolchain: MLflow, Docker, Kubernetes (or managed equivalents), CI/CD (Azure DevOps/GitHub Actions)
• Model monitoring: drift (data + concept), performance, alerting, retraining triggers
• Artifact management: model registry, feature store concepts, reproducibility
• Security/compliance-aware engineering: secrets management, access controls, audit logging

Nice-to-have

• Terraform/IaC, platform enablement experience
• Feature store implementation experience

Familiarity with three lines of defense” / governance in regulated environments