AI Infrastructure Engineer

🕒 2 days ago

🌐 Germany, United Kingdom – Remote

🇩🇪 Germany – Remote 🇬🇧 United Kingdom – Remote

💵 €90k - €105k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

👷 Infrastructure Engineer

👻 Ghost score 10%

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vCluster

WebsiteLinkedInAll Job Openings

51 - 200 employees

☁️ SaaS

🏢 Enterprise

💰 $24M Series A on 2024-05

SaaS • Enterprise

vCluster is a Kubernetes virtualization product (from loft-sh) that creates virtual Kubernetes clusters on top of existing Kubernetes infrastructure. It enables dedicated, multi-tenant or private-node clusters for use cases like internal platform standardization, hybrid Kubernetes deployments, sovereign or dedicated customer environments, AI cloud providers and distributed inference. vCluster is positioned as a tooling/product solution for enterprises operating Kubernetes at scale, offering managed features, releases and integrations for internal K8s platforms and cloud-native workflows.

📋 Description

• 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

🎯 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 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

🏖️ Benefits

• 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