Linux Infrastructure Engineer

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.