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Technical Program Manager - AI Infrastructure / GPU Clusters

Job Description

About the Company

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GMI Cloud is building next- 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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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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