A managed architecture and procurement service that turns an AI workload forecast into a validated GPU? cluster design, vendor plan, and deployment-ready commercial specification.
Added Aug 12, 2026
High opportunity (78%)
Neocloud operators and enterprises building private AI infrastructure must coordinate GPU? capacity, networking, storage, orchestration, security, and data-transfer economics across multiple vendors. These decisions are usually split among engineering, capacity, product, procurement, and sales teams, creating integration gaps, unrealistic utilization commitments, and costly deployment delays.
Provide a fixed-scope deployment-readiness engagement covering workload sizing, reference architecture, storage and network validation, vendor comparison, capacity economics, and implementation sequencing. The business initially operates as a specialist managed service, using benchmark templates and repeatable design reviews to produce a procurement specification, risk register, and acceptance-test plan. Over time, reusable modeling and validation components can become a productized assessment platform.
Training and inference deployments increasingly require coordinated decisions across hardware, storage, networking, and orchestration, while vendors are staffing dedicated roles to bridge those boundaries. Rapidly changing accelerator availability and large capacity commitments make independent design and commercial validation increasingly valuable.
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Our client is a fast-growing AI infrastructure company building next-generation GPU-powered AI platforms. They operate high-performance AI data centers that support large-scale machine learning, AI model training and inference workloads. The environment is highly technical, focusing on low-latency networking, scalability, automation and operational excellence.
AWS operates the world's largest fleet of GPU-accelerated servers powering AI/ML training and inference at cloud scale. Our team defines the server architectures, drives the hardware designs, and owns the fleet quality for these platforms — from component selection through datacenter operations. If you want to shape the physical hardware that frontier models train on, this is the role.
Own end-to-end customer success for complex datacenter GPU deployments, from platform definition and development through cluster bring-up, performance optimization, production deployment, and operational maturity.
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