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Kubernetes; K8S​/HPC Engineer

Remote / Online - Candidates ideally in
Atlanta, Fulton County, Georgia, 30383, USA
Listing for: Capgemini
Remote/Work from Home position
Listed on 2026-10-17
Job specializations:
  • IT/Tech
    Systems Engineer, Cloud Computing: Infrastructure & Operations, SRE/Site Reliability
Salary/Wage Range or Industry Benchmark: 76730 - 150800 USD Yearly USD 76730.00 150800.00 YEAR
Job Description & How to Apply Below
At Capgemini Engineering, the world leader in engineering services, we bring together a global team of engineers, scientists, and architects to help the world’s most innovative companies unleash their potential. From autonomous cars to life-saving robots, our digital and software technology experts think outside the box as they provide unique R&D and engineering services across all industries. Join us for a career full of opportunities.

Where you can make a difference. Where no two days are the same.

Location This role is remote in USAAbout the job you’re considering

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.

Your role 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.

Your skills and experience5+ years of experience in Kubernetes, Cloud Infrastructure, Platform Engineering, Dev Ops, 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.

Exposure to Trainium or Inferentia is highly preferred.

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.

The base compensation range for this role in the posted location is $76,730 to $150,800

Capgemini provides compensation range information in accordance with applicable national, state, provincial, and local pay transparency laws. The base compensation range listed for this position reflects the minimum and maximum target compensation Capgemini, in good faith, believes it may pay for the role at the time of this posting. This range may be subject to change as permitted by law.

The actual compensation offered to any candidate may fall outside of the posted range and will be determined based on multiple factors legally permitted in the applicable jurisdiction.

These may include, but are not limited to:
Geographic location, Education and qualifications, Certifications and licenses, Relevant experience and skills, Seniority and…
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