• Architect, build, and scale the end-to-end ML Ops pipeline, including training, fine-tuning, evaluation, rollout, and monitoring. • Design reliable infrastructure for model deployment, versioning, reproducibility, and orchestration across cloud and on-prem GPU clusters. • Optimize compute usage across distributed systems (Kubernetes, autoscaling, caching, GPU allocation, checkpointing workflows). • Lead the implementation of observability for ML systems (monitor drift, performance, throughput, reliability, cost). • Build automated workflows for dataset curation, labeling, feature pipelines, evaluation, and CI/CD for ML models. • Collaborate with researchers to productionize models and accelerate training/inference pipelines. • Establish ML Ops best practices, internal standards, and cross-team tooling. • Mentor engineers and influence architectural direction across the entire AI platform.