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

Uvation•Las Vegas, NV

About The Position

We are seeking a highly experienced Senior Linux Infrastructure Engineer to design, deploy, operate, and troubleshoot large-scale Linux-based infrastructure supporting enterprise workloads and AI/ML environments. The role emphasizes hands-on expertise in Bare Metal as a Service (BMaaS), GPU infrastructure, high-performance storage, data center operations, and enterprise Linux platforms rather than DevOps-focused work. The engineer will build and manage infrastructure from the hardware layer up, including servers, networking, storage, GPU clusters, and AI-ready platforms. The role supports HPC, AI Factory environments, and large-scale Linux deployments where performance, reliability, and operational excellence are critical.

Requirements

Expert-level Linux administration; Ubuntu is required, while Red Hat and SUSE are preferred.
Deep expertise in bare metal server deployment, architecture, provisioning, and lifecycle management.
Experience operating 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, and hardware diagnostics and troubleshooting.
Experience designing, implementing, and supporting enterprise Linux infrastructure at scale.
Experience deploying and managing GPU-accelerated infrastructure for AI/ML workloads.
Knowledge of NVIDIA GPU technologies, including A100, H100, H200, B200, or equivalent platforms; NVIDIA DGX and OEM GPU servers; and GPU provisioning, lifecycle management, monitoring, and optimization.
Knowledge of AI Factory architecture and infrastructure requirements; experience supporting GPU clusters, AI training environments, and HPC workloads.
Understanding of GPU resource allocation and scheduling, multi-GPU systems, GPU networking requirements, and high-bandwidth, low-latency infrastructure design.
Familiarity with CUDA, NCCL, GPUDirect Storage, and NVIDIA Fabric Manager.
Advanced Linux storage administration, including LVM, XFS, EXT4, NFS, iSCSI, Fibre Channel SAN, and Multipath I/O.
Strong hands-on experience with Ceph, including cluster architecture, MON, OSD, MDS, RBD, CephFS, RGW, capacity planning, performance tuning, and failure recovery.
Understanding of high-performance AI storage platforms, NVMe-over-Fabrics, RDMA, GPUDirect Storage, parallel file systems, and AI data pipelines.
Strong networking knowledge, including bonding, VLANs, routing, MTU optimization, DNS, and DHCP.
Experience with high-performance data center networking, including 100G/200G/400G Ethernet, RoCE, RDMA, and Spine-Leaf architectures.
Strong understanding of Layer 2 and Layer 3 infrastructure design and troubleshooting.
Experience with high availability, clustering, and disaster recovery.
Strong troubleshooting skills across Linux operating systems, hardware platforms, GPU infrastructure, networking, and enterprise storage.
Experience supporting mission-critical production environments.
Bash and Python scripting skills for automation and operational efficiency.
Experience creating operational documentation, runbooks, and infrastructure standards.

Nice To Haves

NVIDIA Base Command or NVIDIA Base Command Manager.
Kubernetes infrastructure, especially AI/ML and GPU integration.
KVM, VMware, OpenShift Virtualization, or similar virtualization platforms.
Ansible automation.
Slurm or other HPC workload schedulers.
Observability and monitoring platforms such as Prometheus, Grafana, and OpenTelemetry.
Data Center Infrastructure Management (DCIM) tools.
IPAM solutions.
AWS, Azure, or hybrid cloud exposure.
Experience with NVIDIA Spectrum-X, Mellanox/NVIDIA ConnectX adapters, or equivalent technologies.
Experience with WEKA, VAST Data, Dell PowerScale, Pure Storage FlashBlade, or NetApp.

Responsibilities

Design, deploy, operate, and troubleshoot large-scale Linux infrastructure.
Deploy, provision, and manage bare metal servers and BMaaS platforms, including hardware lifecycle management.
Design, implement, and support enterprise Linux infrastructure at scale.
Deploy and manage GPU-accelerated infrastructure for AI/ML workloads, including GPU provisioning, lifecycle management, monitoring, and performance optimization.
Support GPU clusters, AI training environments, HPC workloads, and AI Factory infrastructure.
Administer and maintain Linux storage, Ceph, and high-performance AI storage platforms; plan capacity, tune performance, and recover from failures.
Design, implement, and troubleshoot data center networking and high-performance networking infrastructure.
Troubleshoot Linux operating systems, hardware platforms, GPU infrastructure, networking, and enterprise storage.
Support mission-critical production environments, high availability, clustering, and disaster recovery.
Use Bash and Python scripting to improve automation and operational efficiency.
Create and maintain operational documentation, runbooks, infrastructure standards, and SOPs.
Perform incident, problem, and capacity management; proactively identify and mitigate risks.
Support AI cloud integration for workloads and business continuity and disaster recovery planning for AI workloads.
Apply AI infrastructure design and reference architectures.