Senior Kubernetes Platform Engineer - AI/ML Infrastructure

Your Impact / Core Responsibilities

• Architect, build, and operate large-scale on-prem Kubernetes platforms (OpenShift/Anthos), including control plane and etcd lifecycle management
• Define and evolve scalable, multi-tenant platform architecture supporting AI/ML and GPU-based workloads
• Enable and optimize ML workloads including training, inference, and LLM deployment pipelines on Kubernetes
• Build platform extensions using Kubernetes controllers, operators, CRDs, and Golang-based services
• Implement Infrastructure as Code and automation to improve scalability, consistency, and operational efficiency
• Drive AIOps capabilities using telemetry, automation, anomaly detection, and self-healing systems for platform reliability
• Improve observability (metrics, logs, traces) and optimize resource utilization, scheduling, and cluster performance
• Partner with ML engineers and data scientists to operationalize ML workflows and ensure platform readiness for AI workloads
• Participate in on-call rotations, owning incident response, reliability, and continuous operational improvement
• Mentor engineers and contribute to defining platform engineering standards and best practices

Minimum Qualifications

• 8+ years of software engineering experience
• 4+ years of hands-on Kubernetes production experience with control plane ownership
• Strong experience operating on-prem or self-managed Kubernetes environments
• Deep expertise in etcd management (backup, restore, recovery, upgrades)
• Strong proficiency in Go with experience building Kubernetes controllers, operators, CRDs, and webhooks
• Deep understanding of Kubernetes internals (API server, scheduler, controller loops, reconciliation)
• Experience supporting AI/ML or GPU-based workloads on Kubernetes platforms
• Proven experience operating and debugging large-scale distributed systems
• Experience participating in on-call rotations and production incident management

Preferred Qualifications

• Experience with bare-metal or enterprise on-prem infrastructure at scale
• Exposure to AI/ML platforms and tooling (e.g., Kubeflow, MLflow, distributed training systems)
• Experience building internal developer platforms or platform-as-a-service (PaaS) systems
• Familiarity with AIOps concepts such as automated remediation and predictive operations
• Experience applying data-driven or ML-based techniques for system reliability or capacity planning
• Contributions to Kubernetes, CNCF, or other open-source ecosystems