Kubernetes Engineer, HPC

Capgemini

. Design, deploy, and manage Kubernetes-based compute platforms for AI/ML and HPC workloads .

Posted 10/14/2026full-timeRemote • United StatesMid-LevelSenior💰 $76,730 - $150,800 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and managing Kubernetes-based platforms for AI/ML and HPC workloads, with a strong focus on infrastructure automation, CI/CD pipelines, and performance optimization. Proficient in managing containerized applications and ensuring operational excellence in cloud-native environments.

Highest-signal resume keywords

Kubernetes (K8S)HPC Cluster ManagementNVIDIA GPU InfrastructureCI/CD Pipeline ImplementationPython Scripting

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

KubernetesDockerHelmHPCSlurmVolcanoNVIDIA GPULinux AdministrationPythonBash Scripting

Soft Skills

Analytical SkillsProblem-SolvingCross-Functional Collaboration

Tools & Technologies

Azure AKSGoogle Kubernetes Engine (GKE)Workload SchedulersMonitoring SolutionsInfrastructure Automation

Industry Keywords

AI/ML WorkloadsCloud InfrastructurePlatform EngineeringDevOpsDistributed Systems

Tech Stack

Tools & technologies

AzureCloudDistributed SystemsDockerKubernetesLinuxPython

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

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
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

Benefits

Comp & perks

Vacation: 12-25 days, depending on grade
Company paid holidays
Personal Days
Sick Leave
Medical, dental, and vision coverage
Retirement savings plans (e.g., 401(k) in the U.S.)
Life and disability insurance
Employee assistance programs
Other benefits as provided by local policy and eligibility
Potential eligibility for variable incentives, bonuses, or commissions