• Design, train, and optimize ML models using PyTorch or TensorFlow for production-grade applications. • Build scalable data pipelines for feature engineering and model training using Pandas, Dask, or equivalent frameworks. • Implement model evaluation, hyperparameter tuning, and performance monitoring. • Develop and maintain ML workflows using Airflow, Kedro, and MLflow for reproducibility and traceability. • Automate model deployment and lifecycle management across environments (dev, staging, production). • Handle large-scale datasets efficiently using distributed computing frameworks (Dask, Spark). • Ensure data quality, consistency, and compliance with governance standards. • Implement model drift detection, performance tracking, and automated retraining strategies. • Document ML workflows, best practices, and operational guidelines.