Kubernetes Platform Engineer - AI Infrastructure

Kubernetes Platform Engineer - Ai Infrastructure - Hybrid

Join our Platform Engineering team to design, build, and operate large-scale, on-prem Kubernetes infrastructure powering next-generation AI/ML platforms, including GPU-enabled environments for traditional models and LLMs. You will lead the technical direction of scalable, reliable systems, managing the Kubernetes control plane and extending platform capabilities through custom controllers and operators. You'll architect ML platforms, implement Infrastructure as Code with Golang, and drive MLOps best practices. Partnering closely with data scientists and ML engineers, you'll enable high-performance AI workloads while leveraging AIOps for automation and reliability. This role requires strong hands-on on-prem Kubernetes experience and offers opportunities to mentor engineers and influence platform strategy in a hybrid environment.

Your Impact / Responsibilities

• Design, build, and operate large-scale on-prem Kubernetes platforms (OpenShift/Anthos), with ownership of control plane, etc, and cluster lifecycle.
• Architect scalable, multi-tenant platform infrastructure as the foundation for AI/ML and GenAI workloads.
• Enable and optimize AI/ML workloads, including GPU-based environments for training, inference, and model deployment.
• Partner with data scientists and ML engineers to onboard and scale ML pipelines and workflows.
• Build platform capabilities using Kubernetes controllers, operators, CRDs, and Golang/Python services.
• Implement Infrastructure as Code, automation, and AIOps-driven self-healing using platform telemetry and observability.
• Ensure reliability through performance tuning (scheduling, resource utilization) and participate in on-call support and incident response.

Minimum Qualifications

• 5+ years of software engineering experience, including supporting AI/ML or GPU-based workloads on Kubernetes platforms
• 3+ years operating Kubernetes in production with control plane ownership, preferably in on-prem or self-managed environments
• Strong experience with etcd management (backup, restore, recovery) and Kubernetes cluster upgrades
• Proficiency in Go with experience building Kubernetes controllers/operators, CRDs, and webhooks
• Deep understanding of Kubernetes internals (API server, scheduler, controller loops, reconciliation patterns)
• Proven ability to debug and operate large-scale distributed systems in production environments, including participation in on-call rotations

Preferred Qualifications

• Experience with bare-metal or on-prem infrastructure at scale
• Experience enabling or supporting GPU-based workloads in Kubernetes environments
• Familiarity with AI/ML platforms, pipelines, or tooling (e.g., model training, inference, or orchestration)
• Experience building internal developer platforms or platform-as-a-service (PaaS) capabilities
• Exposure to AIOps, including automation, anomaly detection, or self-healing systems
• Experience applying statistical or ML techniques to operational data for reliability, performance, or capacity planning