K8S Lifecycle Automation Eng

Job Description :

Senior Kubernetes Platform Engineer (Zero-Touch GPU Cloud – GitOps Automation)

We are looking for a

Senior Kubernetes Platform Engineer

with 10+ years of infrastructure experience to design and implement the

Zero-Touch Build, Upgrade, and Certification pipeline

for our on-premises GPU cloud platform. This role focuses on automating the Kubernetes layer and its dependencies (e.g., GPU drivers, networking, runtime) using

100% GitOps workflows . You will work across teams to deliver a fully declarative, scalable, and reproducible infrastructure stack—from hardware to Kubernetes and platform services.

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 (e.g.,

NVIDIA GPU Operator )

Container runtime configuration ( Containerd )

Networking layers ( CNI plugins

like Calico, Cilium, etc.)

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 (e.g., cluster conformance, GPU health checks).

Collaborate with infrastructure, security, and SRE teams to ensure seamless handoffs between lower layers (hardware / OS) and the Kubernetes platform.

Mentor junior engineers and contribute to the platform automation roadmap.

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 and its integration in GPU workloads

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 :

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