Kubernetes; K8S​/HPC Engineer

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Where you can make a difference. Where no two days are the same.##
** Location
* * This role is remote based in USA##
** About the job you’re considering
** 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.##

** Your role
*** 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.##
** Your skills and experience
*** 5+ years of experience in Kubernetes, Platform Engineering, Cloud Infrastructure, Dev Ops, 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 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 performance, Market and business consideration, Internal pay equity.

It is not typical for candidates to be hired at or near the top of the posted compensation  addition to base salary, this role may be eligible for additional compensation such as variable…