Linux Infrastructure Engineer (Bare Metal, Storage & AI Factory Infrastructure)

• Linux Infrastructure Engineer (Bare Metal, Storage & AI Factory Infrastructure)

Job Overview

We are seeking a highly experienced Senior Linux Infrastructure Engineer with deep expertise in Linux administration, bare metal infrastructure, enterprise storage, and next-generation AI Factory / GPU infrastructure platforms. This role is focused on designing, deploying, operating, and troubleshooting large-scale Linux-based infrastructure that powers both traditional enterprise workloads and modern AI/ML environments.

This is not a DevOps-focused role. We already have a dedicated DevOps team and are looking for an engineer with extensive hands-on experience in Bare Metal as a Service (BMaaS), GPU infrastructure, high-performance storage, data center operations, and enterprise Linux platforms.

The ideal candidate will have experience building and managing infrastructure from the hardware layer up, including servers, networking, storage, GPU clusters, and AI-ready platforms. They should be comfortable working with high-performance computing (HPC), AI Factory environments, and large-scale Linux deployments where performance, reliability, and operational excellence are critical.

Key Responsibilities & Required Skills

Linux & Bare Metal Infrastructure

• Expert-level Linux administration (Ubuntu required; Red Hat and SUSE preferred)
• Deep expertise in bare metal server deployment, architecture, provisioning, and lifecycle management
• Experience operating Bare Metal as a Service (BMaaS) platforms and large-scale infrastructure environments
• Strong understanding of server hardware, including:
  • BIOS/UEFI
  • RAID controllers
  • Firmware management
  • iLO/iDRAC/IPMI
  • NICs and SmartNICs
  • HBA cards
  • Hardware diagnostics and troubleshooting
• Experience designing, implementing, and supporting enterprise Linux infrastructure at scale

AI Factory & GPU Infrastructure

• Experience deploying and managing GPU-accelerated infrastructure for AI/ML workloads
• Understanding of NVIDIA GPU technologies including:
  • A100, H100, H200, B200, or equivalent GPU platforms
  • NVIDIA DGX and OEM GPU servers
  • GPU provisioning and lifecycle management
  • GPU monitoring and performance optimization
• Knowledge of AI Factory architecture and infrastructure requirements
• Experience supporting GPU clusters, AI training environments, and high-performance computing (HPC) workloads
• Understanding of:
  • GPU resource allocation and scheduling
  • Multi-GPU systems
  • GPU networking requirements
  • High-bandwidth, low-latency infrastructure design
• Familiarity with NVIDIA ecosystem technologies such as:
  • CUDA
  • NCCL
  • GPUDirect Storage
  • NVIDIA Fabric Manager
  • NVIDIA Base Command (preferred)

Enterprise Storage & Data Platforms

• Advanced Linux storage administration:
  • LVM
  • XFS, EXT4
  • NFS
  • iSCSI
  • Fibre Channel SAN
  • Multipath I/O
• Strong hands-on experience with Ceph, including:
  • Cluster architecture
  • MON, OSD, MDS
  • RBD, CephFS, RGW
  • Capacity planning
  • Performance tuning
  • Failure recovery
• Experience with high-performance AI storage platforms such as:
  • WEKA
  • VAST Data
  • Dell PowerScale
  • Pure Storage FlashBlade
  • NetApp
• Understanding of:
  • NVMe-over-Fabrics (NVMe-oF)
  • RDMA
  • GPUDirect Storage
  • Parallel file systems
  • AI data pipelines

Networking & Infrastructure

• Strong networking knowledge:
  • Bonding
  • VLANs
  • Routing
  • MTU optimization
  • DNS
  • DHCP
• Experience with high-performance data center networking:
  • 100G/200G/400G Ethernet
  • RoCE
  • RDMA
  • Spine-Leaf architectures
• Familiarity with NVIDIA Spectrum-X, Mellanox/NVIDIA ConnectX adapters, or equivalent technologies
• Strong understanding of Layer 2 and Layer 3 infrastructure design and troubleshooting

Operations & Reliability

• Experience with high availability, clustering, and disaster recovery
• Strong troubleshooting skills across:
  • Linux operating systems
  • Hardware platforms
  • GPU infrastructure
  • Networking
  • Enterprise storage
• Experience supporting mission-critical production environments
• Bash and Python scripting for automation and operational efficiency
• Experience creating operational documentation, runbooks, and infrastructure standards
• Understanding of AI infrastructure design and reference architectures
  
• AI cloud integration for workloads
• SOP and runbook development and maintenance
• Incident, problem, and capacity management
• Business continuity and disaster recovery planning for AI workloads
• Proactive risk identification and mitigation to avoid business impact

Nice to Have

• Kubernetes infrastructure (especially AI/ML and GPU integration)
• KVM, VMware, OpenShift Virtualization, or similar virtualization platforms
• Ansible automation
• NVIDIA Base Command Manager
• Slurm or HPC workload schedulers
• Observability and monitoring platforms (Prometheus, Grafana, OpenTelemetry)
• Data Center Infrastructure Management (DCIM) tools
• IPAM solutions
• AWS, Azure, or hybrid cloud exposure

We Are Not Looking For

• Candidates whose experience is primarily CI/CD pipeline engineering
• Engineers focused mainly on Terraform, GitOps, or application delivery pipelines
• Cloud-only administrators with limited bare metal, storage, or hardware experience
• Professionals whose primary expertise is software development rather than infrastructure engineering

Ideal Candidate

Someone who has spent years designing, building, and operating enterprise Linux environments, large-scale bare metal infrastructure, storage platforms, and modern AI Factory environments. The ideal candidate understands how to deploy and manage GPU-enabled infrastructure, BMaaS platforms, enterprise storage, and high-performance networking while solving complex operating system, hardware, storage, and AI infrastructure challenges. DevOps experience is a plus, but deep Linux, infrastructure, storage, BMaaS, and AI Factory expertise is the primary requirement.