[Remote] Engineering Manager, GPU Infrastructure

Note: The job is a remote job and is open to candidates in USA. 5C Data Centers operates in the hyperscale, AI data center, and cloud computing industry. The Engineering Manager, GPU Infrastructure will lead the planning, deployment, integration, and operational readiness of large-scale GPU clusters supporting AI training, inference, and high-performance computing workloads across cloud, hybrid, and on-premises environments. The role oversees technical teams and cross-functional programs spanning hardware integration, networking, storage, automation, validation, and operational handoff.

Responsibilities

• Oversee and partake in deployment and integration of GPU-based compute platforms from NVIDIA and other accelerator vendors
• Lead and participate in end-to-end logical deployment of large-scale AI and GPU clusters in state of the art datacenters
• Manage deployment programs spanning compute, storage, networking, power, cooling, and automation layers
• Participate in cluster architecture review for AI training, inference and distributed compute workloads
• Coordinate rack-and-stack and cabling sequencing, network deployment, burn-in testing, and cluster validation.Validate deployment readiness, topology consistency, GPU fabric performance, acceptance testing, and operational turnover processes
• Establish repeatable and documented deployment methodologies and scalable operational standards
• Lead deployment and operational validation of high-performance GPU interconnects using InfiniBand and Ethernet GPU fabric architectures
• Ensure proper implementation of: spile-leaf architectures, RDMA, network telemetry and performance tuning
• Coordinate closely with network engineering teams on topology implementation and performance optimization
• Coordinate with storage engineering teams on deployment and integration of high-performance storage environments supporting AI workloads
• Ensure successful implementation and operational optimization of data storage platforms
• Validate storage throughput, latency, and GPU data delivery performance
• Lead infrastructure automation initiatives for cluster provisioning and lifecycle management
• Manage deployment tooling and orchestration platforms including: Infrastructure-as-Code frameworks Automated imaging and provisioning systems (e.g. Canonical MaaS) Cluster monitoring and observability tools
• Drive standardization and deployment automation to improve speed, reliability, and repeatability
• Build and lead high-performing technical deployment and infrastructure engineering teams
• Partner with datacenter operations, hardware vendors, networking teams, and AI platform engineering groups
• Establish strong Project Management Office (PMO) partnership while driving consistent, accurate project updates across the team and systems (e.g. Jira)
• Develop operational procedures, documentation, and deployment best practices
• Mentor engineers and technical leads across infrastructure domains

Skills

• · Bachelor's degree in Computer Science, Engineering, Information Technology, or related field (or equivalent experience)
• · 10+ years of infrastructure engineering or datacenter deployment experience
• · 5+ years leading deployment or operations teams supporting large-scale AI, HPC, or GPU infrastructure
• · Hands-on experience deploying and operating large GPU clusters in enterprise or hyperscale environments
• · Strong expertise with:
• O Canonical MaaS
• O Data storage platforms
• O InfiniBand and Ethernet GPU fabrics
• O Network architecture
• O Linux systems administration
• O GPU server architectures
• · Strong understanding of:
• O RDMA and RoCE networking
• O High-performance storage architectures
• O Cluster automation and provisioning
• O Datacenter infrastructure operations
• · Proven ability to manage complex cross-functional infrastructure deployment programs
• · Experience deploying NVIDIA DGX SuperPOD or similar AI infrastructure solutions
• · Familiarity with:
• O NVIDIA networking technologies
• O Spectrum-X or Quantum platforms
• O AI model training infrastructure
• O Liquid cooling environments
• O DCIM and observability platforms
• · Experience in hyperscale, cloud, or AI infrastructure environments
• · Certifications in networking, Linux, Kubernetes, or cloud infrastructure are a plus

Benefits

• Remote work (US, Pacific Time Zone)
• Health, Dental & Vision
• 401(k)
• Life Insurance
• Voluntary Benefits
• PTO

Company Overview