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

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)

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.