Senior Kubernetes Engineer

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. 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.

Senior Kubernetes Engineer
Location: Dallas, TX (Hybrid –3/2) | Relocation available | Remote 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.