Cloud Infrastructure Architect

20 hours ago

Dombivli, Maharashtra, India beBeeKubernetes Full time ₹ 1,50,00,000 - ₹ 2,02,50,000

About our company: We provide digital engineering and technology services to global clients.

Our team prioritizes early execution for impactful results. We combine logic, creativity, and curiosity to build, solve, and create.

Job Title: Senior Kubernetes Platform Engineer

Job Description:

Platform Design and Implementation

Design and implement the zero-touch build, upgrade, and certification pipeline for our on-premises GPU cloud platform using GitOps workflows.

Key Responsibilities
  • Architect and implement GitOps-driven Kubernetes cluster lifecycle automation using tools like kubeadm, ClusterAPI, Helm, and Argo CD.
  • Develop and manage declarative infrastructure components for GPU stack deployment, container runtime configuration, networking layers, and lead automation efforts to enable zero-touch upgrades and certification pipelines.
  • 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.
  • Collaborate with infrastructure, security, and SRE teams to ensure seamless handoffs between lower layers and the Kubernetes platform.
Requirements
  • 8+ 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, Argo CD for GitOps-based delivery, Helm for application and cluster add-on packaging, Containerd as a container runtime, and NVIDIA GPU Operator.
  • Strong GitOps mindset with experience managing infrastructure as code through Git-based workflows.
  • Experience building Kubernetes clusters in on-prem environments and 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) and familiarity with Linux internals, systemd, and OS-level tuning for container workloads.

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