Trains and fine-tunes ML models, builds data preprocessing and feature engineering pipelines, deploys models as REST APIs, integrates inference into production applications, and designs RAG and LLM-powered systems. Covers MLOps workflows including experiment tracking, drift detection, retraining triggers, and A/B testing. Use when the user asks about training or fine-tuning a model, building ML pipelines, model serving or inference optimization, evaluating model performance, working with frameworks like PyTorch, TensorFlow, scikit-learn, or Hugging Face, setting up vector databases, prompt engineering, or taking an ML prototype to production.