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

Uvation; Oregon; Hybrid; USD 160,000 - 230,000; Full time

Job summary

Uvation, Inc. is seeking a Senior Linux Infrastructure Engineer to design, deploy, operate, and optimize large-scale, GPU-enabled Linux environments.

You will own BMaaS provisioning, hardware lifecycle, and storage architectures for AI Factory workloads and HPC environments. The role emphasizes hands-on hardware, kernel-level tuning, and high-availability operations across data center platforms, with a focus on reliability and performance.

Qualifications

• Extensive hands-on Linux administration across enterprise platforms.
• Proficiency with Ubuntu; Red Hat and SUSE experience preferred.
• Deep hands-on in BMaaS, bare metal provisioning and lifecycle management.
• Experience designing and operating GPU-enabled infrastructure for AI/ML workloads.
• Strong storage expertise (Ceph, NVMe, SAN) and high-performance networking.
• Familiarity with HPC and AI Factory environments; disaster recovery planning.

Responsibilities

• Design, deploy, operate and troubleshoot large-scale Linux-based infrastructure.
• Lead BMaaS deployment, hardware provisioning, and lifecycle management.
• Build and manage GPU clusters, AI-ready platforms, and HPC workloads.
• Administer enterprise storage (Ceph, NVMe, SAS/SAN) and networking (100G/400G).
• Develop runbooks, automation scripts (Bash/Python) and incident response playbooks.
• Monitor performance, diagnose issues, and drive reliability and uptime.

Skills

Linux administration

Bare metal provisioning

BMaaS

GPU infrastructure

Ceph storage

Networking (100G/400G)

HPC/AI workflows

Python scripting

Bash scripting

Tools

iLO/iDRAC

NVMe/Storage hardware

DGX/NVIDIA GPUs

Ceph tooling

Prometheus/Grafana

Job description

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/UEFIRAID controllers
• Firmware management
• iLO/iDRAC/IPMINICs 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:
• CUDANCCLGPUDirect StorageNVIDIA Fabric ManagerNVIDIA Base Command (preferred)
• Enterprise Storage & Data Platforms
• Advanced Linux storage administration:
• LVMXFS, EXT4NFSiSCSIFibre Channel SANMultipath I/O
• Strong hands-on experience with
• Ceph
• , including:
• Cluster architectureMON, OSD, MDSRBD, CephFS, RGWCapacity planningPerformance tuningFailure recovery
• Experience with high-performance AI storage platforms such as:
• WEKAVAST DataDell PowerScalePure Storage FlashBladeNetApp
• Understanding of:
• NVMe-over-Fabrics (NVMe-oF)RDMAGPUDirect StorageParallel file systemsAI data pipelines
• Networking & Infrastructure
• Strong networking knowledge:
• BondingVLANsRoutingMTU optimizationDNSDHCP
• Experience with high-performance data center networking:
• 100G/200G/400G EthernetRoCERDMASpine-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 systemsHardware platformsGPU infrastructureNetworkingEnterprise 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 workloadsSOP and runbook development and maintenanceIncident, problem, and capacity managementBusiness continuity and disaster recovery planning for AI workloadsProactive 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) toolsIPAM solutions
• AWS, Azure, or hybrid cloud exposure

We Are Not Looking For

• Candidates whose experience is primarily CI/CD pipeline engineeringEngineers focused mainly on Terraform, GitOps, or application delivery pipelinesCloud-only administrators with limited bare metal, storage, or hardware experienceProfessionals 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.