AI Infrastructure Engineer

vCluster is a venture-backed tech startup pioneering Kubernetes virtualization for the AI era. As an AI Infrastructure Engineer, you will lead technical deployments and optimize infrastructure for GPU AI Clouds and enterprises, ensuring a seamless experience for customers.

Responsibilities:

• Lead Technical Deployments: Drive end-to-end technical deployments for GPU neocloud and AI Factory customers, from initial bare metal configuration to a validated vCluster environment
• Infrastructure Optimization: Configure and troubleshoot bare metal GPU node infrastructure, including CNI configuration, GPU Operator setup, distributed storage backends, and RDMA/InfiniBand
• Validation: Deploy and validate Kubernetes and vCluster to provide GPU-powered managed K8s
• Knowledge Transfer: Work alongside customer teams to build self-sufficiency, ensuring they can operate and grow the platform independently
• Scaling through Documentation: Document reusable playbooks and deployment architectures so your learnings become the next customer's head start
• Feedback Loop: Collaborate with Engineering and Product to surface recurring infrastructure challenges, acting as a direct feedback loop from the field into the roadmap
• Strategic Partnering: Join Sales in the pre-sales process where deep infrastructure work is required to achieve a meaningful proof of value

Requirements:

• 5+ years of experience deploying and operating Kubernetes in production, ideally on bare metal or in high-complexity environments
• Practical knowledge of NVIDIA GPU Operators, CUDA tooling, and systems-level configuration for GPU nodes
• Deep understanding of CNI plugins, overlay networks, load balancing, and connectivity diagnosis in layered environments
• Experience with persistent volume configuration, CSI drivers, and distributed systems like Ceph, Rook, Weka, or Longhorn
• Comfort operating in ambiguous, fast-moving environments where you are often writing the playbook in real time
• You thrive in environments that reject legacy tech and prefer a modern stack where you can solve a variety of problems from pipelines to internal services
• Experience writing automation scripts with Bash, Python, or Go
• Relevant certifications such as CKA (Certified Kubernetes Administrator) or experience writing Kubernetes Operators
• Experience with inference serving, GPU scheduling, and the tooling around LLM deployment
• Experience building AI Automation in documentation to contribute to a shared knowledge base