Linux Infrastructure Engineer – Bare Metal, Storage, AI Factory Infrastructure

atUvationRemote

Nevada Freelance Infrastructure EngineerMid-levelSenior

Posted 3 days ago

This is a fully remote position, open to applicants in Nevada.

📋 Description

• Design, implement, operate, and troubleshoot extensive Linux-based infrastructures tailored for enterprise and AI/ML workloads.

• Construct and oversee infrastructure from the hardware level upwards, encompassing servers, networking, storage, GPU clusters, and AI-optimized platforms.

• Deploy and manage GPU-accelerated infrastructures, including GPU clusters, AI training environments, and HPC workloads.

• Provision and manage the lifecycle of bare metal servers, BMaaS platforms, GPU systems, and enterprise infrastructure.

• Design, implement, and support large-scale enterprise Linux infrastructure.

• Administer and enhance enterprise storage solutions, Ceph clusters, high-performance AI storage, parallel file systems, and AI data pipelines.

• Design and troubleshoot high-performance data center networks and Layer 2/Layer 3 infrastructure.

• Monitor and optimize performance of GPU and infrastructure.

• Support mission-critical production environments ensuring high availability, clustering, and disaster recovery.

• Troubleshoot issues related to Linux operating systems, hardware, GPU infrastructure, networking, and storage.

• Automate operational tasks employing Bash and Python scripting.

• Create and maintain operational documentation, SOPs, runbooks, and standards for infrastructure.

• Manage incidents, problems, capacity, business continuity, and disaster recovery planning for AI workloads.

• Proactively identify and mitigate risks to avoid any business impact.

• Collaborate with the existing DevOps team, emphasizing infrastructure engineering over CI/CD or application delivery.

⛳️ Requirements

• Expert-level knowledge in Linux administration; proficiency in Ubuntu is mandatory, with Red Hat and SUSE as preferred.

• Extensive experience in bare metal server deployment, architecture, provisioning, and lifecycle management.

• Familiarity with operating Bare Metal as a Service (BMaaS) platforms and large-scale infrastructure environments.

• Strong understanding of server hardware components such as BIOS/UEFI, RAID controllers, firmware management, iLO/iDRAC/IPMI, NICs and SmartNICs, HBA cards, and hardware diagnostics.

• Proficient in designing, implementing, and supporting enterprise Linux infrastructure at scale.

• Experience in deploying and managing GPU-accelerated infrastructure for AI/ML workloads.

• Knowledge of NVIDIA GPU technologies, including A100, H100, H200, B200, or equivalent platforms.

• Experience with NVIDIA DGX and OEM GPU servers, GPU provisioning, lifecycle management, monitoring, and performance optimization.

• Understanding of AI Factory architecture and its infrastructure requirements.

• Experience in supporting GPU clusters, AI training environments, and HPC workloads.

• Knowledge of GPU resource allocation and scheduling, multi-GPU systems, GPU networking, and the design of high-bandwidth, low-latency infrastructure.

• Familiarity with CUDA, NCCL, GPUDirect Storage, NVIDIA Fabric Manager, and preferably NVIDIA Base Command.

• Advanced skills in Linux storage administration: 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.

• Experience with high-performance AI storage platforms like WEKA, VAST Data, Dell PowerScale, Pure Storage FlashBlade, and NetApp.

• Understanding of NVMe-over-Fabrics, RDMA, GPUDirect Storage, parallel file systems, and AI data pipelines.

• Solid networking knowledge covering bonding, VLANs, routing, MTU optimization, DNS, and DHCP.

• Experience with 100G/200G/400G Ethernet, RoCE, RDMA, spine-leaf architectures, NVIDIA Spectrum-X, Mellanox/NVIDIA ConnectX adapters, or equivalent technologies.

• Strong grasp of Layer 2 and Layer 3 infrastructure design and troubleshooting methodologies.

• Experience with high availability, clustering, and disaster recovery protocols.

• Proficient troubleshooting skills across Linux operating systems, hardware platforms, GPU infrastructure, networking, and enterprise storage.

• Proficient in Bash and Python scripting.

• Experience in the creation of operational documentation, runbooks, and infrastructure standards.

• Understanding of AI infrastructure design, reference architectures, and AI cloud integration.

• Experience in the development and maintenance of SOPs and runbooks, incident, problem, and capacity management, business continuity and disaster recovery planning, as well as proactive risk mitigation.

• Nice to have: Familiarity with Kubernetes infrastructure, KVM/VMware/OpenShift virtualization, Ansible, NVIDIA Base Command Manager, Slurm or HPC schedulers, Prometheus/Grafana/OpenTelemetry, DCIM, IPAM, and exposure to AWS/Azure/hybrid cloud environments.

• Not seeking candidates who primarily focus on CI/CD, Terraform, GitOps, application delivery pipelines, cloud-only administration, or software development.

• Deep expertise in Linux, infrastructure, storage, BMaaS, and AI Factory is the primary qualification required.