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# Kubernetes (K8S) / HPC Engineer

- **Company:** Capgemini
- **Location:** Atlanta, GA, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $76,730.0 - $150,800.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Application Performance Management, Computing Platforms, Bash Shell, Big Data, Cloud Computing, Cloud Engineering, Continuous Delivery, Continuous Integration, DevOps, Distributed Systems, Python (Programming Language), Linux System Administration, Machine Learning, Performance Tuning, Reliability Engineering, Prometheus, Azure Machine Learning, Planning Software, Data Logging, High Performance Computing, Grafana, HybridCloud, AWS Trainium, Containerization, Kubernetes, Infrastructure Automation Frameworks, Azure AKS, Slurm, Hardware Infrastructure, Terraform, Serverless Computing, Docker
- **Published:** October 23, 2026
- **Apply:** https://www.atlantacareerpath.com/job.asp?id=3466478129&tx=JK2519FFG&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

We are seeking an experienced Kubernetes / HPC Engineer with 5+ years of experience in Kubernetes, cloud infrastructure, and platform engineering to design, deploy, and support scalable compute platforms for AI/ML, simulation, and high-performance computing workloads. This role will focus on building reliable, secure, and high-performance infrastructure across cloud and on-premises environments while enabling next-generation AI, research, and data-intensive applications., * 5+ years of experience in Kubernetes, Platform Engineering, Cloud Infrastructure, DevOps, Site Reliability Engineering, or related technical disciplines.

 * 2+ years of experience supporting High-Performance Computing (HPC) environments, distributed computing platforms, or large-scale compute clusters.
 * Strong hands-on expertise with Kubernetes, Docker, Helm, and container orchestration technologies.
 * Experience managing HPC clusters and workload schedulers, including Slurm, Volcano, or similar scheduling platforms.
 * Expertise with NVIDIA GPU infrastructure, GPU-enabled workloads, performance optimization, and accelerated computing environments.
 * Strong Linux administration, troubleshooting, and automation skills, including Python and Bash scripting.
 * Experience implementing CI/CD pipelines, monitoring, logging, observability, and infrastructure automation solutions.
 * Hands-on experience with major cloud platforms, including Azure Kubernetes Service (AKS), Google Kubernetes Engine (GKE), or comparable cloud-native services.
 * Strong understanding of distributed systems, cloud-native architectures, scalability, reliability, and performance tuning.

Good to have skills

 * Experience supporting AI/ML platforms and large-scale model training workloads.
 * Exposure to AWS Trainium, AWS Inferentia, or AI accelerator technologies.
 * Experience with Infrastructure-as-Code tools such as Terraform.
 * Knowledge of cloud networking, storage systems, and hybrid cloud architectures.
 * Experience with observability platforms such as Prometheus and Grafana

## Description

* Design, deploy, and manage Kubernetes-based platforms supporting AI/ML, HPC, and distributed computing workloads.
 * Build, administer, and optimize HPC environments utilizing workload schedulers such as Slurm and Volcano.
 * Deploy and support NVIDIA GPU infrastructure for machine learning, simulation, and accelerated computing workloads.
 * Implement infrastructure automation, CI/CD pipelines, and Infrastructure-as-Code practices to improve platform scalability and operational efficiency.
 * Manage containerized applications using Kubernetes, Docker, Helm, and cloud-native technologies.
 * Troubleshoot and optimize cluster, network, storage, and application performance across distributed computing environments.