NexGen Cloud - Solution Architect - GPU & HPC

• Rather than a long checklist, here’s what success in this role looks like: • 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. • 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. • Collaborate closely with Pre-Sales Engineering, Data Centre, Infrastructure, and Network Engineering teams to validate delivery feasibility before commitments are made to customers. • 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. • Develop high-quality technical proposals, RFP responses, and statements of work, supporting commercial discussions with clear scoping, realistic estimates, and honest risk assessments. • Define and maintain comprehensive Bills of Materials (BoMs) for all proposed solutions, ensuring accuracy for procurement, provisioning, and margin review. • Feed back recurring customer requirements and competitive intelligence to engineering leadership, directly influencing the Hyperstack product roadmap. • 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: • Essential • 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. • 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. • Hands-on experience optimising AI and HPC workloads across multi-GPU and multi-node configurations — including profiling, bottleneck identification, and performance tuning at both the application and infrastructure layer. • Strong working knowledge of containerisation and orchestration in HPC/AI contexts: Docker, Kubernetes, NVIDIA GPU Operator, and container-native workload management. • Background in an OEM, hyperscaler, neo-cloud, or enterprise/research HPC environment, with demonstrable exposure to the full design-to-deployment lifecycle for GPU-accelerated workloads. • Ability to produce clear, professional technical documentation and architecture diagrams suitable for both engineering and board-level audiences. • Confident engaging with customers, vendors, and internal engineering teams as a technical authority — able to translate complex software and performance trade-offs into clear, actionable decisions.