Senior Kubernetes Engineer

Senior Kubernetes Engineer Location: Dallas, TX (Hybrid – 3/2) | Relocation available Type: Direct Hire • Competitive base salary + performance bonus • 100% company-paid benefits 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 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 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.