Working knowledge of MLOps and production ML practices, including experiment tracking, model registry, CI/CD for ML, model deployment, monitoring, drift detection, retraining, and tools such as MLflow, Airflow, Docker, Kubernetes, Git, or cloud-native ML platforms. Build and optimize supervised, unsupervised, deep learning, NLP, and Generative AI models using structured and unstructured data such as claims history, medical records, claim notes, policy data, and operational data.