• Build and maintain CI/CD pipelines for machine learning, focusing on automated testing, model deployment, and version control (using tools like MLflow or Git). • Deploy ML models as scalable APIs and microservices, ensuring they meet performance and latency requirements for clinical use. • Implement basic monitoring tools to track model performance, data drift, and system health in production. • Develop and optimize ETL processes to transform healthcare data (FHIR, HL7) into clean, usable datasets for model training and inference. • Help build and maintain feature stores and data layers that ensure consistency between training and production environments. • Work closely with backend teams to integrate ML outputs into our core healthcare applications. • Write clean, maintainable, and well-documented Python code. • Participate in code reviews to ensure system reliability. • Use Docker and Kubernetes to package and orchestrate ML workloads across different environments. • Follow established protocols to ensure all data handling and deployments meet HIPAA and HITRUST security standards.