Senior Kubernetes Platform Engineer - AI/ML Infrastructure
Required skills
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 skills
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
Technologies
Kubernetes, OpenShift, Anthos, Go, etcd, AI/ML platforms, GPU-based workloads, Kubeflow, MLflow, AIOps, Infrastructure as Code
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
Seniority
Senior
Domain
AI/ML Infrastructure