Kubernetes (K8S) / HPC Engineer

Capgemini•Atlanta, GA

4d•$76,730 - $150,800•Remote

About The Position

As a Kubernetes (K8S) / HPC Engineer, you will be responsible for designing, deploying, and supporting scalable Kubernetes and High-Performance Computing (HPC) platforms that power AI/ML, simulation, and other compute-intensive workloads. You will work closely with platform engineering, data science, and research teams to deliver reliable, high-performance infrastructure across cloud and on-premises environments.

Requirements

5+ years of experience in Kubernetes, Cloud Infrastructure, Platform Engineering, DevOps, or related fields.
Minimum 2+ years of experience supporting HPC or large-scale distributed compute environments.
Strong hands-on experience with Kubernetes (K8S), Docker, Helm, and container orchestration technologies.
Experience managing HPC clusters and workload schedulers, including Slurm and/or Volcano.
Expertise with NVIDIA GPU infrastructure, GPU-enabled workloads, and performance optimization.
Strong Linux administration and troubleshooting skills.
Proficiency in Python and/or Bash scripting for automation and operational management.
Experience implementing CI/CD pipelines, monitoring, logging, and infrastructure automation solutions.
Hands-on experience with at least one major cloud platform, Azure AKS and Google Kubernetes Engine (GKE)
Strong understanding of distributed systems, cloud-native architectures, scalability, and performance tuning.
Excellent analytical, problem-solving, and cross-functional collaboration skills.

Nice To Haves

Exposure to Trainium or Inferentia is highly preferred.

Responsibilities

Design, deploy, and manage Kubernetes-based compute platforms for AI/ML and HPC workloads.
Build and maintain scalable HPC environments utilizing workload schedulers.
Support and optimize NVIDIA GPU infrastructure for machine learning, data processing, and simulation workloads.
Implement infrastructure automation, CI/CD pipelines, and monitoring solutions to improve platform reliability and operational efficiency.
Manage containerized applications using Kubernetes, Docker, and Helm.
Troubleshoot and optimize cluster, network, storage, and application performance across distributed computing environments.
Collaborate with engineering, research, and AI teams to enable high-performance computing and cloud-native solutions.
Ensure platform scalability, availability, security, and operational excellence.