Senior Kubernetes Engineer - HPC / GPU

Senior Kubernetes Engineer (GPU / AI Platforms)

Location: Dallas, TX (Hybrid)

Type: Direct Hire

• Competitive base salary performance bonus

• 100% company-paid benefits

• Relocation available

Overview

We are seeking a Senior Kubernetes Engineer to design, build, and optimize GPU-accelerated container platforms supporting large-scale HPC, AI/ML workloads, and next-generation CaaS / GPUaaS environments.

This role is focused on enabling scalable, multi-tenant compute platforms that power GPU-as-a-Service (GPUaaS) and Container-as-a-Service (CaaS) offerings across hybrid and on-prem infrastructure. You will work at the intersection of Kubernetes and the NVIDIA ecosystem, driving performance, efficiency, and reliability for high-throughput, GPU-intensive workloads.

The ideal candidate brings deep hands-on experience building production-grade Kubernetes platforms for AI and HPC workloads, along with strong development skills and a passion for high-performance, distributed systems at scale.

Key Responsibilities

Kubernetes Platform Engineering

• Architect, deploy, and operate Kubernetes clusters optimized for GPU-intensive and multi-tenant workloads
• Design platforms supporting CaaS / GPUaaS delivery models, ensuring scalability, resilience, and performance
• Leverage NVIDIA GPU Operator, Network Operator, and DCGM for cluster management and observability

GPU Enablement & Scheduling

• Integrate NVIDIA device plugins, MIG, and GPU sharing capabilities into Kubernetes scheduling frameworks
• Optimize GPU utilization and workload placement using scheduler extensions (kube-scheduler plugins, Slurm, Volcano)
• Support AI/ML training, LLM workloads, and scientific computing at scale

Automation & Platform Development

• Develop and maintain Kubernetes operators and custom controllers
• Automate platform provisioning and lifecycle management using Go or Python
• Implement Infrastructure-as-Code using Terraform, Helm, and Kustomize

Observability & Performance Optimization

• Implement monitoring and telemetry using Prometheus, Grafana, DCGM Exporter, and OpenTelemetry
• Drive performance tuning, capacity planning, and optimization across GPU-enabled clusters
• Support incident response and ensure production readiness

Security & Multi-Tenancy

• Design secure, multi-tenant Kubernetes environments using RBAC, namespaces, and policy enforcement (OPA, Gatekeeper)
• Ensure workload isolation and governance across shared GPU infrastructure
• Support secure platform operations across CaaS / GPUaaS environments

DevOps & CI/CD

• Build and maintain CI/CD pipelines using GitOps tools such as ArgoCD and FluxCD
• Support continuous delivery and lifecycle management of Kubernetes-based platforms

Cross-Functional Collaboration

• Partner with HPC, AI/ML, DevOps, and platform engineering teams to support high-performance workloads
• Collaborate on platform architecture, optimization strategies, and operational best practices

Required Experience

• Extensive experience operating Kubernetes in production-scale environments
• Strong experience supporting HPC, AI/ML, or GPU-accelerated infrastructure
• Experience designing or supporting CaaS, GPUaaS, or multi-tenant platform environments
• Deep expertise with NVIDIA and Kubernetes ecosystems including GPU Operator, device plugins, NVML, MIG, and DCGM
• Strong understanding of Kubernetes internals (CRDs, RBAC, controllers, scheduler extensions)
• Proficiency in Go or Python for automation and operator development
• Experience supporting GPU-intensive workloads (LLMs, AI/ML pipelines, HPC applications)
• Hands-on experience with Helm, Kustomize, and GitOps workflows

Technical Skills

• Monitoring and observability: Prometheus, Grafana, DCGM Exporter, OpenTelemetry
• Networking: CNI plugins (NVIDIA CNI, Multus), service networking, cluster networking concepts
• Infrastructure-as-Code: Terraform, Helm, Kustomize
• CI/CD and GitOps practices

Preferred Experience

• Experience with container runtimes (containerd, CRI-O, NVIDIA Container Toolkit)
• Exposure to advanced networking solutions such as Cilium
• Contributions to open-source projects within Kubernetes or NVIDIA ecosystems
• Experience working in large-scale HPC or AI infrastructure environments

Additional Requirements

• This position requires applicants to be currently authorized to work in the U.S. without employer sponsorship.
• We are unable to sponsor or take over sponsorship of employment visas at this time.