Design, operate, and improve on-premises MLOps infrastructure to productionize, deploy, monitor, and optimize ML models. Troubleshoot across the stack (Linux, Docker, Kubernetes), refine CI/CD workflows, integrate training/validation testing, and collaborate with data scientists and engineers to ensure reliability and performance at scale.