Technical Program Manager - AI Infrastructure / GPU Clusters

Job Description

About the Company

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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.

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About the Role

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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.

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Responsibilities

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GPU Cluster Deployment

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

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

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

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

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

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

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

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

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