GPU Cloud Infrastructure Specialist

2 weeks ago

Ahmedabad, Gujarat, India beBeeKubernetes Full time US$ 1,20,000 - US$ 1,80,000

Job Title: Senior Kubernetes Platform Engineer

About the Role:

We are seeking an experienced Senior Kubernetes Platform Engineer to join our team. The successful candidate will be responsible for designing and implementing a Zero-Touch Build, Upgrade, and Certification pipeline for our on-premises GPU cloud platform.

The ideal candidate will have 10+ years of hands-on experience in infrastructure engineering, with a strong focus on Kubernetes-based environments. They will also have expertise in Kubernetes cluster management, Argo CD GitOps integration, Helm templating, Go/Python scripting, and Containerd.

Responsibilities include architecting and implementing GitOps-driven Kubernetes cluster lifecycle automation using tools like kubeadm, ClusterAPI, Helm, and Argo CD. Additionally, the engineer will develop and manage declarative infrastructure components for GPU stack deployment, container runtime configuration, networking layers, and more.

The Senior Kubernetes Platform Engineer will work closely with cross-functional teams to deliver a fully declarative, scalable, and reproducible infrastructure stack. This includes standardizing deployment practices across multi-cluster GPU environments and driving observability, testing, and validation as part of the continuous delivery process.

A solid understanding of CNI plugin ecosystems, network policies, and multi-tenant networking in Kubernetes is essential. Experience building Kubernetes clusters in on-prem environments and managing high availability and security in multi-cluster, GPU-accelerated workloads is also required.

If you are a motivated and detail-oriented individual with excellent problem-solving skills, we encourage you to apply for this exciting opportunity.

  • Design and implement a Zero-Touch Build, Upgrade, and Certification pipeline for our on-premises GPU cloud platform.
  • Architect and implement GitOps-driven Kubernetes cluster lifecycle automation.
  • Develop and manage declarative infrastructure components for GPU stack deployment, container runtime configuration, and networking layers.
  • Standardize deployment practices across multi-cluster GPU environments.
  • Drive observability, testing, and validation as part of the continuous delivery process.

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