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.