MLOps Senior [Risk Team]

Requisitos

• 3+ years in ML Engineering, Data Engineering, or DevOps with hands-on deployment/maintenance of ML systems in production
• Experience building ML training pipelines with experiment tracking and model registry (MLflow, W&B, or similar)
• Understanding of feature store concepts and train-serve consistency challenges
• Strong Python skills
• understanding of ML frameworks enough to package, serve, and debug models
• Experience with Docker and ML pipeline orchestration (Kubeflow, Argo Workflows, Metaflow, or similar)