Built a production-ready end-to-end MLOps pipeline covering data ingestion, feature engineering, model training/tuning, MLflow experiment tracking, FastAPI model serving, A/B testing with champion/challenger routing, drift monitoring, and automated retraining. Orchestrated with Airflow and validated through CI with GitHub Actions.