About The Role

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

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.