vCluster Labs
AI
AIInfrastructureEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“AI Infrastructure Engineer at vCluster Labs. Skills: Kubernetes, GPU Infrastructure, AI Cloud, AI Factory, vCluster. 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.”
What You'll Achieve.
reach production; scale the motion for the next hire and customer; make that happen; provide GPU-powered managed K8s; build self-sufficiency; accelerate time to value; reduce operational burden; maximize the ROI of every GPU
Industry & Context.
solve a variety of problems from pipelines to internal services; grit to push through obstacles; do whatever it takes to figure it out
What They're Looking For.
Must Have
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.
Nice to Have
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.
What You'll Do.
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
distributed storage backends
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.
How You'll Work.
Team & Collaboration
Work alongside customer teams to build self-sufficiency; Collaborate with Engineering and Product to surface recurring infrastructure challenges; Join Sales in the pre-sales process
Full Job Description
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:
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