converting notebooks into modular, tested, production-ready code. Deploy models into batch and real-time environments, managing versioning, promotion, rollback, and scheduled workflows via MLflow and APIs. Implement monitoring and observability, including data and model drift detection, performance alerts, logging, and automated retraining. Collaborate with Data Engineering and Platform teams … Maintain engineering standards, ensuring high-quality testing, documentation, code quality, reproducibility, and operational reliability. Your skills and experience: Experience with ML platforms including Databricks, MLflow, Delta Lake, and cloud environments. Proficient in Python, PySpark, and SQL, following production coding best practices. Understanding of data, distributed ML pipelines, and model deployment ...