Key responsibilities :-Client requirement discovery: lead workshops with client CIO/CTO and infrastructure teams to capture workloads, density profiles, power and cooling needs, network, interconnection and sovereignty requirements, and growth phasing HPC/AI solution design: architect end-to-end solutions for AI training and inference environments GPU compute cluster sizing and topology, storage throughput, and high-performance network fabrics mapped onto colocation space, power and cooling. Colocation engineering design: develop space and rack layouts, power topologies (N / N+1 / 2 N), and cooling strategies including rear-door heat exchangers and direct-to-chip liquid cooling for 30–130 k W racks.– GPU & Infini Band architecture: define rack-scale and pod-scale designs for NVIDIA HGX/DGX-class systems, including Infini Band and Ro CE fabric topologies (rail-optimised, fat-tree), cable plant planning and density zoning. Proposals & RFPs: author solution documents, BOMs,
technical proposals and RFP/RFI responses; present and defend designs to client executive and engineering audiences. Techno-commercial support: partner with the commercial team on capacity pricing, fit-out scope splits (shell / powered shell / fully fitted), phasing and SLAs. Build alignment: work with the DC Infrastructure arm to ensure designs are buildable, costed and aligned to campus master plans. Strategic support: lead technical due diligence for anchor tenants and JV partners; mentor junior solution architects and build the solution engineering playbook. Essential qualifications & experience (must-have)10–15 years in data centre solution architecture, pre-sales engineering or design consulting, with a minimum of 3 years dedicated to HPC/AI solution design – designing, dimensioning and deploying GPU compute environments for AI training and inference workloads. Hands-on GPU and Infini Band experience: NVIDIA HGX/DGX-class cluster architecture, Infini Band (NDR/HDR) and Ro CE fabric design, NVL