Senior Kubernetes Engineer

GTN Technical Staffing; Dallas (TX); On-site; USD 150,000 - 210,000; Full time

Job summary

GTN Technical Staffing seeks a Senior Kubernetes Engineer to design and scale a GPU-accelerated compute platform, supporting AI, ML, and HPC workloads. You will own large-scale Kubernetes clusters across on‑prem and hybrid environments.

You will partner with platform, HPC, and ML teams to deliver reliable, multi-tenant compute at scale, extending Kubernetes with custom operators and CRDs, and integrating NVIDIA stack components to optimize performance and efficiency.

Qualifications

• Extensive experience operating Kubernetes in large-scale production environments.
• Hands-on work with NVIDIA GPU ecosystem including GPU Operator and device plugins.
• Proficiency in Go or Python for building Kubernetes operators.

Responsibilities

• Design, deploy and operate large-scale Kubernetes clusters for GPU workloads.
• Architect container platforms for AI/ML, LLMs, and HPC use cases.
• Extend Kubernetes via operators and CRDs for automation.
• Integrate NVIDIA ecosystem components and GPU scheduling strategies.
• Drive performance, reliability and incident response.

Skills

Kubernetes

GPU workloads

Go or Python

CI/CD

Go/Python for operators

Tools

GPU Operator

ArgoCD

FluxCD

Terraform

Kustomize

Job description

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.