Senior Manager, AI Cluster Deployment

5C Data Centers USA, Inc.•Springfield, OH

5d•Remote

About The Position

We are seeking an experienced Senior Manager of AI Cluster Deployment to lead the planning, deployment, integration, and operational readiness of large-scale AI infrastructure environments. This role is responsible for delivering production-grade GPU clusters that support AI training, inference, and high-performance computing workloads across cloud, hybrid, and on-premises environments. The ideal candidate brings deep technical expertise in GPU infrastructure, networking, storage, automation, and datacenter deployment, combined with strong program leadership and cross-functional execution skills. This leader will oversee end-to-end AI cluster deployment initiatives, including hardware integration, rack-and-stack operations, provisioning automation, performance validation, and operational handoff. The role requires hands-on familiarity with modern AI infrastructure tooling and architectures, including Canonical MaaS, VAST Data storage platforms, and both InfiniBand and Ethernet-based GPU networking fabrics.

Requirements

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: Canonical MaaS, Data storage platforms, InfiniBand and Ethernet GPU fabrics, Network architecture, Linux systems administration, GPU server architectures
Strong understanding of: RDMA and RoCE networking, High-performance storage architectures, Cluster automation and provisioning, Datacenter infrastructure operations
Proven ability to manage complex cross-functional infrastructure deployment programs.

Nice To Haves

Experience deploying NVIDIA DGX SuperPOD or similar AI infrastructure solutions.
Familiarity with: NVIDIA networking technologies, Spectrum-X or Quantum platforms, AI model training infrastructure, Liquid cooling environments, DCIM and observability platforms
Experience in hyperscale, cloud, or AI infrastructure environments.
Certifications in networking, Linux, Kubernetes, or cloud infrastructure are a plus.

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.