AI Infrastructure Engineer

• Lead end-to-end technical deployments for GPU neocloud and AI Factory customers, from bare metal configuration to validated vCluster environments
• Configure and troubleshoot bare metal GPU node infrastructure, including CNI configuration, GPU Operator setup, distributed storage backends, and RDMA/InfiniBand
• Deploy and validate Kubernetes and vCluster for GPU-powered managed Kubernetes
• Work with customer teams to build operational self-sufficiency
• Document reusable playbooks and deployment architectures
• Collaborate with Engineering and Product to surface infrastructure challenges and inform the roadmap
• Join Sales in pre-sales proof-of-value engagements requiring deep infrastructure expertise

• 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 such as Ceph, Rook, Weka, or Longhorn
• Comfort operating in ambiguous, fast-moving environments
• Preference for modern technology stacks and solving problems from pipelines to internal services
• Experience writing automation scripts with Bash, Python, or Go
• Relevant certifications such as CKA or experience writing Kubernetes Operators
• Experience with inference serving, GPU scheduling, and LLM deployment tooling
• Experience building AI Automation in documentation and contributing to a shared knowledge base

• Competitive compensation package, including equity
• Health, dental, vision, and life insurance for you and eligible dependents (benefits vary depending on country)
• Flexible working schedule
• Workplace flexibility
• Remote-first culture