AI Storage Infrastructure Engineer

Who you are

• 7+ years of experience in SRE or hardware/storage infrastructure operations
• 5+ years of building/operating distributed production Storage systems at scale
• 7+ years of experience in Linux and K8s storage fundamentals (NFS, CSI)
• 1+ years of experience integrating with or building High Performance Storage solutions (VAST, Weka, DDN, PowerScale)

What the job involves

• The role is to deploy, integrate, and operate high-performance storage for GPU-accelerated compute and AI platforms
• You will own the storage layer where Kubernetes meets bare metal — standing up NFS-based high-performance storage, wiring it into clusters via CSI, and tuning it to keep data flowing to GPU workloads at scale
• Work spans hybrid, edge, and air-gapped deployments built on the Mirantis K0rdent stack
• We are looking for a senior systems engineer who treats storage as infrastructure to be automated, observed, and tuned — not hand-managed
• The right candidate is fluent in Kubernetes storage, deeply versed in Linux storage and networking fundamentals down to the kernel and NFS-client layer, and knows how to make high-performance NAS actually perform under demanding workloads
• You should reach for infrastructure-as-code and GitOps by default, be self-directed in diagnosing performance and reliability issues end to end, set operational standards for others to follow, and communicate clearly across teams
• Bare-metal hardware experience is a strong plus, but deep Linux storage knowledge is essential
• Integrate NFS-based high-performance storage (e.g., VAST, Dell PowerScale) into Kubernetes clusters via CSI, storage classes, and persistent volumes
• Tune the NFS data path — mount options, nconnect/RDMA, Linux client, and network settings — for high-throughput, low-latency GPU/AI workloads
• Deploy and operate storage services and operators; manage capacity, quotas, snapshots, and lifecycle
• Configure and optimize Linux systems for storage workloads, including driver setup, file system layout, network tuning, and kernel parameter optimization
• Deliver storage integration for k0s-based Kubernetes via Cluster API (CAPI) and K0rdent management/child cluster topologies
• Operate storage in fully disconnected (air-gapped) environments, including local artifact/mirror connectivity (Harbor) and PKI/TLS considerations
• Automate storage provisioning and configuration with infrastructure-as-code (Terraform/OpenTofu) and GitOps pipelines (ArgoCD or Flux)
• Build monitoring, alerting, and observability for storage performance, capacity, and health
• Diagnose and resolve performance, reliability, and scaling issues across the storage stack