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

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.

. 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

About Uvation

• Networking & Infrastructure
• 100G/200G/400G Ethernet
• RoCE

Responsibilities

• Bare Metal as a Service (BMaaS) platforms and large-scale infrastructure environments
• 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
• 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
• Multi-GPU systems
• GPUDirect Storage
• NVIDIA Fabric Manager
• NVIDIA Base Command (preferred)
• Enterprise Storage & Data Platforms
• Cluster architecture
• RBD, CephFS, RGW
• Capacity planning
• Failure recovery
• Experience with high-performance AI storage platforms such as:
• Dell PowerScale
• Pure Storage FlashBlade
• NetApp
• VLANs
• 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
• Proactive risk identification and mitigation to avoid business impact

Requirements

• not a DevOps-focused role
• 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.
• 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
• A100, H100, H200, B200, or equivalent GPU platforms
• GPU networking requirements
• High-bandwidth, low-latency infrastructure design
• XFS, EXT4
• Multipath I/O
• Parallel file systems
• AI data pipelines

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
• Linux operating systems
• NVIDIA Base Command Manager
• Slurm or HPC workload schedulers

Candidates whose experience is primarily CI/CD pipeline engineering

• Cloud-only administrators with limited bare metal, storage, or hardware experience
• Professionals whose primary expertise is software development rather than infrastructure engineering
• 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.

Preferred

• Hardware platforms
• Experience supporting mission-critical production environments
• Bash and Python scripting for automation and operational efficiency

Kubernetes infrastructure (especially AI/ML and GPU integration)

• KVM, VMware, OpenShift Virtualization, or similar virtualization platforms
• Observability and monitoring platforms (Prometheus, Grafana, OpenTelemetry)
• Data Center Infrastructure Management (DCIM) tools
• IPAM solutions
• AWS, Azure, or hybrid cloud exposure

Benefits

• GPU infrastructure
• Networking
• Enterprise storage
• Business continuity and disaster recovery planning for AI workloads

Understanding of

GPU resource allocation and scheduling

NFS

• iSCSI
• Fibre Channel SAN
• Strong hands-on experience with
• Ceph

Understanding of

NVMe-over-Fabrics (NVMe-oF)

RDMA

GPUDirect Storage

Strong networking knowledge

• Bonding
• Routing

We Are Not Looking For

Engineers focused mainly on Terraform, GitOps, or application delivery pipelines