Solution Architect - GPU & HPC

TL;DR

Solution Architect - GPU & HPC (GPU cloud): Own the technical sales cycle for GPU cloud opportunities by translating customer workload requirements into technically sound, commercially viable solution designs with an accent on HPC/AI workload architecture, multi-GPU/multi-node performance tuning, and delivery feasibility. Focus on building reference architectures and producing detailed proposals, RFP responses, and BoMs that can be committed and delivered to the required standard.

Location: UK-based (Customer-site travel required)

Company

hirify.global builds Hyperstack, an AI cloud delivering on-demand and private GPU infrastructure for compute-intensive workloads.

What you will do

• Lead the technical sales cycle end-to-end: customer brief, architecture design, proposal, and delivery handover as primary technical authority for GPU cloud solutions.
• Engage with customers to capture workload requirements, technical constraints, and commercial objectives; produce detailed solution designs (architecture diagrams, network topology, storage configurations, GPU resource allocation models).
• Collaborate with Pre-Sales Engineering and infrastructure/network teams to validate delivery feasibility before commitments are made.
• Maintain a library of reference architectures and solution templates across AI/ML training, inference, HPC, and rendering workloads.
• Create high-quality technical proposals, RFP responses, and statements of work with realistic estimates and risk assessments.
• Define and maintain Bills of Materials (BoMs) for proposed solutions and feed customer requirements/competitive intelligence into engineering leadership.

Requirements

• Proven experience designing and delivering HPC or AI software stacks at scale, including workload profiling, scheduler configuration (SLURM/PBS or equivalent), MPI/NCCL tuning, and distributed training frameworks (PyTorch/JAX/DeepSpeed).
• Deep understanding of GPU software environments: CUDA, cuDNN, NCCL, driver stacks, and production tooling for reliable large-scale training and inference.
• Hands-on performance optimization for AI/HPC workloads across multi-GPU and multi-node setups, including bottleneck identification and tuning at both application and infrastructure layers.
• Strong knowledge of containerization 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 exposure to the full design-to-deployment lifecycle for GPU-accelerated workloads.
• Ability to produce clear technical documentation and architecture diagrams for both engineering and board-level audiences.

Nice to have

• Experience with large-scale cluster benchmarking (e.g., NCCL tests, MLPerf) across GPU generations and topologies.
• Exposure to MLOps tooling and AI platform layers (MLflow/W&B, Triton/vLLM, Kubeflow/Airflow).
• Familiarity with InfiniBand and high-performance networking for distributed training performance.
• Commercial awareness from contributing to BoMs, proposals, or RFP responses in a pre-sales/customer-facing technical role.

Culture & Benefits

• Competitive salary with an annual discretionary bonus scheme.
• Employee wellbeing benefits and 25 days of holiday plus public holidays.
• Flexible working with regular customer-site travel as part of the role.
• Ownership and autonomy with direct influence over technical win rate on strategic opportunities.
• Work on cutting-edge GPU cloud infrastructure for AI/ML and HPC workloads.
• Collaborative international culture focused on trust, transparency, and ownership.

Hiring process

• Not specified in the provided text.