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)…