MLOps Engineer: ML Risk Platform

Design and operate automated training, validation, and deployment pipelines. Manage containerized ML workloads on Kubernetes, handle model versioning with MLflow, and build observability for data and prediction drift. Ideal for candidates with experience in regulated environments like fintech or insurance.

• Design and operate automated ML pipelines
• Manage containerized ML workloads on Kubernetes
• Handle model versioning and registry management (e.g., MLflow)
• Build observability for data and prediction drift

• Experience in regulated environments (fintech, insurance)
• Knowledge of MLOps and containerization
• Experience with Kubernetes and MLflow