AI Infrastructure Engineer

As vCluster’s AI Infrastructure Specialist , you will work directly with customers at the earliest and most critical stage of their journey: from bare metal GPU nodes through to a production-ready deployment. This is not a traditional professional services role; you operate pre-sale as part of a proof of value engagement scoped to reach production. You will be one of the first team members a neocloud or AI Factory engages with at a technical depth, and the playbooks you develop will scale the motion for the next hire and customer. vCluster is gaining rapid traction with GPU AI Clouds and enterprises building AI Factories: organizations that need to offer Kubernetes as a managed service on bare metal GPU infrastructure, and need to do it fast. This role exists to make that happen. As an AI Infrastructure Engineer, your role will include: 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. This role could be a fit for you if you bring: Production K8s Mastery: 5+ years of experience deploying and operating Kubernetes in production, ideally on bare metal or in high-complexity environments. GPU Fluency: Practical knowledge of NVIDIA GPU Operators, CUDA tooling, and systems‑level configuration for GPU nodes. Networking Fundamentals: Deep understanding of CNI plugins, overlay networks, load balancing, and connectivity diagnosis in layered environments. Storage Expertise: Experience with persistent volume configuration, CSI drivers, and distributed systems like Ceph, Rook, Weka, or Longhorn. Operational Agility: Comfort operating in ambiguous, fast-moving environments where you are often writing the playbook in real time. Modern Tech Mindset: 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. Bonus points for: Automation Skills: Experience writing automation scripts with Bash, Python, or Go. Kubernetes Depth: Relevant certifications such as CKA (Certified Kubernetes Administrator) or experience writing Kubernetes Operators. AI/ML Familiarity: Experience with inference serving, GPU scheduling, and the tooling around LLM deployment. Documentation: Experience building AI Automation in documentation to contribute to a shared knowledge base. Benefits We offer the following benefits: Competitive Salary : We offer a competitive compensation package, including equity. Platinum-Level Insurance : Health, dental, vision, and life Insurance, including plans for you and eligible dependents (benefits vary depending on country). Flexible Working Schedule : You have a doctor’s appointment or need to head to the supermarket to get groceries at 2pm? We won’t have an issue with that. To us, results matter more than clocking in and out at the same time every day. Workplace Flexibility : We’re very flexible about where you work. We know things can change in life and we’re happy to adjust the work environment for you along the way. #J-18808-Ljbffr