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

uvation

IndiaremotePosted 1 month ago
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Skill Required

Linux-Infrastructure-EngineeringBare-Metal-InfrastructureAI-Factory-InfrastructureGPU-Infrastructure-EngineeringStorage-EngineeringLinux-Infrastructure-EngineerLinux-Infrastructure-SpecialistInfrastructure-Linux-Unix-AnalystSenior-AI-Infrastructure-EngineerAI-Infrastructure-EngineerAI-ML-Infrastructure-EngineerMachine-Learning-Infrastructure-EngineerInfrastructure-Platform-EngineerInfrastructure-EngineerAI EngineerInfrastructure EngineerLinuxAIQuery OptimizationBackup and RecoverytroubleshootingVMwareObservabilityEngineeringKubernetesPrometheusnetworkingautomationTerraformdesigningFirmwarebuildingAnsiblePythondesignDevOpsArgoCDAzureCI/CDCloudShell ScriptingContract

Key highlights

  • Senior‑level role requiring deep Linux, bare‑metal, storage, and AI/ML GPU expertise.
  • Focus on designing, deploying, and operating large‑scale Linux infrastructure (not DevOps).
  • Must have hands‑on experience with BMaaS platforms and enterprise GPU clusters.
  • Expert‑level Linux administration (Ubuntu required; Red Hat and SUSE preferred).
  • Extensive experience with enterprise storage solutions such as Ceph, WEKA, VAST Data, and NetApp.
  • Opportunity to work on cutting‑edge AI Factory and high‑performance GPU infrastructure.

Role 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 focuses on designing, deploying, operating, and troubleshooting large‑scale Linux‑based infrastructure that powers both traditional enterprise workloads and modern AI/ML environments. It is not a DevOps‑focused position; a dedicated DevOps team already exists, and we need 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.

Responsibilities

  • Design, deploy, operate, and troubleshoot large‑scale Linux‑based infrastructure for enterprise and AI/ML workloads.
  • Design, implement, and support enterprise Linux infrastructure at scale.
  • Deploy and manage GPU‑accelerated infrastructure for AI/ML workloads, including provisioning, lifecycle management, monitoring, and performance optimization.
  • Plan capacity, tune performance, and execute failure recovery for enterprise storage systems.
  • Develop Bash and Python scripts to automate operational tasks and improve efficiency.
  • Create and maintain operational documentation, runbooks, and infrastructure standards.
  • Support high availability, clustering, and disaster recovery across Linux, hardware, GPU, networking, and storage components.
  • Collaborate with cross‑functional teams to ensure reliability and operational excellence of the infrastructure.

Requirements

  • Highly experienced senior‑level Linux Infrastructure Engineer with deep expertise in Linux administration, bare metal infrastructure, enterprise storage, and AI Factory/GPU platforms.
  • Expert‑level Linux administration (Ubuntu required; Red Hat and SUSE preferred).
  • Deep expertise in bare metal server deployment, architecture, provisioning, and lifecycle management.
  • Hands‑on experience operating Bare Metal as a Service (BMaaS) platforms and large‑scale infrastructure environments.
  • Proficiency with BIOS/UEFI, RAID controllers, firmware management, iLO/iDRAC/IPMI, NICs and SmartNICs, HBA cards, and hardware diagnostics/troubleshooting.
  • Experience designing, implementing, and supporting enterprise Linux infrastructure at scale.
  • Experience deploying and managing GPU‑accelerated infrastructure for AI/ML workloads, including A100, H100, H200, B200 or equivalent GPUs, NVIDIA DGX and OEM GPU servers.
  • GPU provisioning, lifecycle management, monitoring, performance optimization, and resource allocation/scheduling.
  • Knowledge of AI Factory architecture, high‑bandwidth/low‑latency infrastructure design, CUDA, NCCL, GPUDirect Storage, NVIDIA Fabric Manager, and NVIDIA Base Command (preferred).
  • Enterprise storage expertise: LVM, XFS, EXT4, NFS, iSCSI, Fibre Channel SAN, Multipath I/O, Ceph (MON, OSD, MDS, RBD, CephFS, RGW), capacity planning, performance tuning, failure recovery, WEKA, VAST Data, Dell PowerScale, Pure Storage FlashBlade, NetApp, NVMe‑oF, RDMA, parallel file systems, AI data pipelines.
  • Networking expertise: bonding, VLANs, routing, MTU optimization, DNS, DHCP, 100G/200G/400G Ethernet, RoCE, RDMA, spine‑leaf architectures, familiarity with NVIDIA Spectrum‑X, Mellanox/NVIDIA ConnectX adapters, strong understanding of Layer 2 and Layer 3 design and troubleshooting.
  • Experience with high availability, clustering, and disaster recovery for Linux operating systems, hardware platforms, GPU infrastructure, networking, and enterprise storage.
  • Proficiency in Bash and Python scripting for automation and operational efficiency.
  • Ability to create operational documentation, runbooks, and infrastructure standards.

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

Additional details

  • We are not looking for candidates whose experience is primarily CI/CD pipeline engineering.
  • Engineers focused mainly on Terraform, GitOps, or application delivery pipelines are not desired.
  • Cloud‑only administrators with limited bare metal, storage, or hardware experience are not a fit.
  • Professionals whose primary expertise is software development rather than infrastructure engineering are not sought.
  • 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.
  • DevOps experience is a plus, but deep Linux, infrastructure, storage, BMaaS, and AI Factory expertise is the primary requirement.
  • Originally posted on Himalayas.
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