Senior Kubernetes Engineer

We are seeking a Senior Kubernetes Engineer to help design and scale a next-generation GPU-accelerated compute platform supporting AI, machine learning, and high-performance computing workloads. This role sits at the core of a rapidly expanding infrastructure environment, focused on building high-throughput, highly efficient container platforms across on-prem and hybrid environments.

You will play a key role in architecting and operating large-scale Kubernetes clusters optimized for GPU workloads, working closely with platform, HPC, and ML engineering teams to deliver reliable, multi-tenant compute at scale. This is a hands-on engineering role with strong ownership across performance, automation, and platform evolution., Kubernetes Platform Engineering *Design, deploy, and operate large-scale Kubernetes clusters optimized for GPU-intensive workloads *Architect container platforms supporting AI/ML, LLM training, and HPC use cases *Extend Kubernetes through custom operators, controllers, and CRDs to support infrastructure automation

GPU & Workload Optimization *Integrate and optimize NVIDIA ecosystem components, including GPU Operator, DCGM, and device plugins *Implement GPU scheduling strategies, including MIG, sharing, and workload placement optimization *Enhance cluster efficiency using scheduler extensions such as kube-scheduler plugins, Slurm, or Volcano

Platform Performance & Reliability *Drive performance tuning across compute, networking, and storage layers for high-throughput workloads *Partner with HPC and ML teams to ensure scalability, reliability, and workload efficiency *Participate in production readiness, incident response, and continuous improvement initiatives

Observability & Automation *Implement monitoring and telemetry solutions using Prometheus, Grafana, DCGM Exporter, and OpenTelemetry *Build and maintain CI/CD pipelines for infrastructure using GitOps tools such as ArgoCD and FluxCD *Contribute to infrastructure-as-code using Terraform, Helm, and Kustomize

Security & Multi-Tenancy *Design and enforce secure multi-tenant environments with namespace isolation, RBAC, and policy controls *Implement governance frameworks using tools such as OPA or Gatekeeper *Ensure compliance with platform security and operational standards

Artificial Intelligence (AI), Computer Systems, GPU (Graphics Processing Unit), Machine Learning, High Throughput, Automation, Use Cases, Ecosystems, Web Client Plug-ins, Schedule Development, *Strong experience operating Kubernetes in large-scale, production environments *Hands-on experience with NVIDIA GPU ecosystem, including GPU Operator, device plugins, MIG, and DCGM *Proficiency in Go or Python for building Kubernetes operators and automation tooling *Deep understanding of Kubernetes internals, including CRDs, controllers, RBAC, and scheduling *Experience supporting GPU-intensive workloads such as AI/ML training, LLMs, or scientific computing *Experience with GitOps, CI/CD pipelines, and infrastructure-as-code practices *Familiarity with container networking, including CNI plugins such as NVIDIA CNI or Multus *Experience with monitoring and observability tools for cluster and GPU performance

This is a high-impact opportunity to work at the forefront of AI infrastructure, helping build and scale the platforms that power next-generation compute.