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Solution Architect - GPU & HPC

Nexgencloud Sourced

London, United Kingdom Full-time Not specified

No account needed — this role is applied for on the employer’s own site.

About the role

<h1>Solutions Architect</h1> <p><strong>Location: </strong>UK-based — Customer-Site Travel Required</p> <h2>ABOUT NEXGEN CLOUD</h2> <p>NexGen Cloud is the company behind Hyperstack, a full-stack AI cloud serving tens of thousands of customers from AI researchers to enterprises running the world’s most compute-intensive workloads. We deliver on-demand and private GPU infrastructure to teams who treat performance as a requirement, not a feature.</p> <p>We’re a tight-knit, fast-moving team working at the cutting edge of AI cloud infrastructure. We practise what we preach, equipping our people with AI at every level so we can solve harder problems, ship faster, and keep raising the bar for what enterprise GPU infrastructure looks like.</p> <h2>THE ROLE: Solutions Architect</h2> <p>This role exists because our pipeline of technically complex, high-value GPU cloud opportunities is growing — and winning them requires more than a great sales team. The Solutions Architect sits at the intersection of sales, infrastructure, and the customer, translating complex workload requirements into technically sound, commercially viable solutions on the Hyperstack platform.</p> <p>You’ll be the primary technical authority through the sales cycle: engaging directly with prospective and existing customers, producing detailed solution designs, and ensuring that what is proposed can actually be delivered to the standard committed. This is a role for someone who is equally comfortable in a customer meeting and a technical design review — someone who earns trust through depth, not slides.</p> <h2>WHAT YOU’LL BE DOING</h2> <p>Rather than a long checklist, here’s what success in this role looks like:</p> <ul> <li>Own the technical sales cycle end-to-end — from initial customer brief through architecture design, proposal, and delivery handover — acting as the primary technical authority for GPU cloud solution design.</li> <li>Engage directly with prospective and existing customers to understand workload requirements, technical constraints, and commercial objectives, producing detailed solution designs including architecture diagrams, network topology, storage configurations, and GPU resource allocation models.</li> <li>Collaborate closely with Pre-Sales Engineering, Data Centre, Infrastructure, and Network Engineering teams to validate delivery feasibility before commitments are made to customers.</li> <li>Build and maintain a library of reference architectures and solution templates spanning AI/ML training, inference, HPC, and rendering workloads — accelerating the sales cycle and improving proposal consistency across the team.</li> <li>Develop high-quality technical proposals, RFP responses, and statements of work, supporting commercial discussions with clear scoping, realistic estimates, and honest risk assessments.</li> <li>Define and maintain comprehensive Bills of Materials (BoMs) for all proposed solutions, ensuring accuracy for procurement, provisioning, and margin review.</li> <li>Feed back recurring customer requirements and competitive intelligence to engineering leadership, directly influencing the Hyperstack product roadmap.</li> </ul> <h2>ABOUT YOU</h2> <p>We’re more interested in how you think and work than in a perfect CV. You’ll likely come from an HPC, AI infrastructure, or high-performance cloud background and bring a combination of the following:</p> <h3>Essential</h3> <ul> <li>Proven experience in HPC or AI software stack design and delivery at scale — including workload profiling, scheduler configuration (SLURM, PBS, or equivalent), MPI/NCCL tuning, and distributed training frameworks such as PyTorch, JAX, or DeepSpeed.</li> <li>Deep understanding of GPU software environments: CUDA, cuDNN, NCCL, driver stacks, and the tooling required to run large-scale AI training and inference workloads reliably in production.</li> <li>Hands-on experience optimising AI and HPC workloads across multi-GPU and multi-node configurations — including profi

Skills

Infrastructure

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