GPU Cloud Infrastructure Engineer

2 weeks ago

Vizag, Andhra Pradesh, India beBeeKubernetes Full time ₹ 1,50,00,000 - ₹ 2,50,00,000
About the Role

We're seeking an experienced infrastructure engineer to spearhead the development and implementation of our Zero-Touch Build, Upgrade, and Certification pipeline for our on-premises GPU cloud platform.

This is a unique opportunity to leverage your expertise in Kubernetes cluster management and automation to drive innovation and growth within our organization.

Key Responsibilities
  • Design and implement GitOps-driven Kubernetes cluster lifecycle automation using tools like kubeadm, ClusterAPI, Helm, and Argo CD.
  • Develop declarative infrastructure components for GPU stack deployment, container runtime configuration, and networking layers.
  • Lead automation efforts to enable zero-touch upgrades and certification pipelines for Kubernetes clusters and associated workloads.
  • Maintain Git-backed sources of truth for all platform configurations and integrations.
  • Standardize deployment practices across multi-cluster GPU environments, ensuring scalability, repeatability, and compliance.
  • Drive observability, testing, and validation as part of the continuous delivery process.
Required Skills & Experience
  • 10+ years of hands-on experience in infrastructure engineering, with a strong focus on Kubernetes-based environments.
  • Primary key skills required are Kubernetes API, Helm templating, Argo CD GitOps integration, Go/Python scripting, Containerd.
  • Deep knowledge and hands-on experience with: Kubernetes cluster management (kubeadm, ClusterAPI), Argo CD for GitOps-based delivery, Helm for application and cluster add-on packaging, Containerd as a container runtime.
  • Experience deploying and operating the NVIDIA GPU Operator or equivalent in production environments.
  • Solid understanding of CNI plugin ecosystems, network policies, and multi-tenant networking in Kubernetes.
  • Strong GitOps mindset with experience managing infrastructure as code through Git-based workflows.
  • Experience building Kubernetes clusters in on-prem environments (vs. managed cloud services).
  • Proven ability to scale and manage multi-cluster, GPU-accelerated workloads with high availability and security.
  • Solid scripting and automation skills (Bash, Python, or Go).
  • Familiarity with Linux internals, systemd, and OS-level tuning for container workloads.
Bonus Requirements
  • Experience with custom controllers, operators, or Kubernetes API extensions.
  • Contributions to Kubernetes or CNCF projects.
  • Exposure to service meshes, ingress controllers, or workload identity providers.

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