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

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.

Originally posted on Himalayas