Maintain and improve on-premises MLOps infrastructure: deploy, monitor, and optimize ML models; troubleshoot Linux/Docker/Kubernetes stacks; build CI/CD, deployment, and monitoring tools; productionize research artifacts; enforce testing and engineering standards; collaborate with data scientists and software engineers to scale ML systems.