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

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.

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

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

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

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

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)

CUDA

NCCL

GPUDirect Storage

NVIDIA Fabric Manager

NVIDIA Base Command (preferred)

Advanced Linux storage administration: LVM XFS, EXT4 NFS iSCSI Fibre Channel SAN Multipath I/O

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

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

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

NVMe-over-Fabrics (NVMe-oF)

RDMA

Parallel file systems

AI data pipelines

Strong networking knowledge: Bonding VLANs Routing MTU optimization DNS DHCP

Bonding

VLANs

Routing

MTU optimization

DNS

DHCP

Experience with high-performance data center networking: 100G/200G/400G Ethernet RoCE RDMA Spine-Leaf architectures

100G/200G/400G Ethernet

RoCE

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

Experience with high availability, clustering, and disaster recovery

Strong troubleshooting skills across: Linux operating systems Hardware platforms GPU infrastructure Networking Enterprise storage

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

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

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.