ML Engineer (MLOps) Senior [Risk Team]
Plata CardCore Responsibilities
Conduct evaluation of Feature Store / Feature Registry solutions (Feast, Tecton, or custom), prepare a recommendation, design the architecture and feature lifecycle process (experiment → stable → production). Lead the implementation by engineering and platform teams Design, build, and own ML training and deployment pipelines: experiment tracking, model registry, CI/CD for models, packaging and handoff to production.
Requirements
3+ years of experience in ML Engineering, Data Engineering, or DevOps with hands-on involvement in deploying and maintaining 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.
Additional Information
Experience Level
Mid-Level
Job Language
English
Employment Type
Full-time
Work Mode
Remote