The ideal candidate will be responsible for developing high-quality applications. They will also be responsible for designing and implementing testable and scalable code.
Role: Kubernetes (K8S) / HPC Engineer
Location: USA – Remote
Start Date: Immediate
Background Check: Mandatory
Key Responsibilities:
Design, deploy, and manage scalable Kubernetes and HPC compute platforms.
Support AI/ML, engineering simulation, scientific computing, and data-intensive workloads.
Manage Kubernetes clusters using Docker, Helm, and container orchestration technologies.
Administer HPC clusters and workload schedulers such as Slurm/Volcano.
Support NVIDIA GPU infrastructure, AI/ML workloads, and performance optimization.
Manage Linux-based infrastructure and automate deployments using Python/Bash.
Implement CI/CD, monitoring, infrastructure automation, and cloud-native solutions.
Troubleshoot and optimize large-scale distributed compute environments.
Required Skills:
5+ years of experience in Kubernetes / Cloud Infrastructure.
2+ years of experience with HPC or large-scale compute platforms.
Strong hands-on experience with Kubernetes, Docker, Helm, and Linux.
Experience with Slurm, Volcano, or similar workload schedulers.
Strong knowledge of GPU infrastructure, preferably NVIDIA, and AI/ML workloads.
Proficiency in Python and/or Bash scripting.
Experience with CI/CD, monitoring, and infrastructure automation.
Experience with AWS EKS, Azure AKS, or GCP GKE.
Preferred Skills:
RDMA / InfiniBand and high-speed networking.
Parallel/distributed storage systems.
Large-scale GPU/HPC environments.
Experience supporting engineering simulations, semiconductor workloads, AI model training, or scientific computing.
Ideal Candidate:
A hands-on Kubernetes/HPC Infrastructure Engineer who can manage both cloud-native container platforms and large-scale HPC/GPU environments, with strong Linux administration, automation, troubleshooting, and performance optimization skills.