ml-engineer

Build production ML systems: model training pipelines, serving infrastructure, performance optimization, and automated retraining. Use when: (1) designing or building ML pipelines (data validation → training → deployment), (2) optimizing model training (hyperparameter search, distributed training, checkpointing), (3) deploying models to production (REST/gRPC endpoints, batch/stream processing, canary releases), (4) setting up ML monitoring (prediction drift, feature drift, performance decay), (5) implementing feature engineering or feature stores, (6) automating retraining triggers, (7) debugging model performance or serving latency issues. Triggers on: ML pipeline, model training, model serving, feature engineering, hyperparameter tuning, model deployment, inference optimization, model monitoring, MLOps, retraining.