Type: Direct Hire
- Competitive base salary + performance bonus
Overview
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.
Key Responsibilities
- 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
- 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
Required Experience
- 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.