Saurabh presents proven platform patterns for running AI workloads reliably on Kubernetes, with a focus on admission, isolation, and observability. The session dives into how admission policies enforce GPU quotas and fairness, how tenant isolation prevents noisy-neighbor issues, and how observability patterns make AI jobs debuggable in production. Attendees will see real manifests, admission controllers, and quota strategies that balance utilization and stability. The talk also covers service mesh–based isolation with Istio, OpenTelemetry-driven tracing and metrics, and policy frameworks for safe multi-tenant AI. Lessons learned include preventing GPU starvation, reducing operator toil, and improving user trust in GenAI platforms. The session closes with a practical checklist attendees can adopt immediately, built entirely with CNCF and open source tools.