Technical Program Manager - AI Infrastructure / GPU Clusters (United States)

About the Company

GMI Cloud is building next-generation AI infrastructure designed for large-scale GPU training and inference workloads. Our platform supports high-density GPU clusters deployed in modern data centers across multiple regions.

About the Role

We are looking for a Technical Program Manager (TPM) to drive the deployment and delivery of GPU cluster infrastructure. This role will work at the intersection of AI hardware platforms, high-performance networking, and data center infrastructure, coordinating across solution architects, engineering teams, vendors, and contractors to deliver production-ready AI clusters.

Responsibilities

GPU Cluster Deployment

• Lead the end-to-end deployment of AI GPU clusters, from infrastructure planning through production launch.
• Drive coordination across Infrastructure Solution Architects, network engineers, hardware vendors, and data center teams.
• Manage delivery timelines covering hardware deployment, network integration, cluster bring-up, and production readiness.

Infrastructure Architecture Collaboration

• Work closely with Infrastructure Solution Architects (SA) to define:
• GPU server platform selection
• Network architecture for distributed GPU clusters
• Storage integration and cluster infrastructure design
• Support development of the cluster Bill of Materials (BOM) including compute, networking, storage, and supporting infrastructure components.
• Ensure architecture decisions align with data center constraints such as power density, cooling capacity, and rack layout.

System Integration

• Drive system integration for large-scale GPU clusters, including:
• Rack elevation planning
• GPU server deployment and configuration
• High-speed network topology implementation
• Power and cooling readiness
• Ensure deployments align with vendor reference architectures and validated cluster designs.

Contractor & Field Deployment Management

• Work closely with General Contractors (GC) and system integrators to manage on-site infrastructure implementation.
• Lead contractor onboarding, including SOW development, scope definition, and delivery milestone alignment.
• Coordinate and oversee field deployment activities such as:
• Structured cabling installation
• Rack installation and equipment mounting
• Network and power connectivity preparation
• Hardware staging and deployment logistics

Cluster Validation & Performance Testing

• Coordinate cluster bring-up and validation activities including:
• Single-node GPU validation
• Multi-node cluster deployment
• GPU interconnect validation (P2P, RDMA)
• Drive cluster benchmarking, stress testing, and performance verification before production release.

Operational Readiness

• Ensure deployed GPU clusters are fully ready for production workloads by driving:
• Hardware and network validation
• Monitoring and telemetry integration
• Operational documentation and runbooks
• Handover to operations teams

Qualifications

• 5+ years experience in Technical Program Management, Infrastructure Program Management, or HPC infrastructure delivery
• Experience with GPU cluster deployments or high-performance computing environments
• Familiarity with GPU server architecture and distributed computing infrastructure
• Experience working with Infrastructure Solution Architects to define system architecture and hardware BOM
• Experience managing data center hardware deployments and system integration
• Ability to coordinate multi-vendor infrastructure projects across regions

Required Skills

• Experience deploying large-scale AI infrastructure or GPU clusters
• Familiarity with:
• InfiniBand / RoCE / high-speed Ethernet networking
• GPU interconnect validation (P2P / RDMA)
• Rack elevation and high-density rack deployment
• Experience with cluster validation and performance benchmarking
• Background as Systems Engineer, HPC Engineer, or Infrastructure Architect
• Experience working in AI infrastructure, cloud infrastructure, or hyperscale data centers

Preferred Skills

• Experience deploying liquid-cooled GPU clusters or high-power racks
• Experience working with NVIDIA AI infrastructure platforms
• Familiarity with AI training environments and distributed workloads