Solutions Architect - AI Infrastructure
Salary: £90,000 – £110,000 base + 20% annual bonus
Function: Pre-Sales / Infrastructure Sales
We're looking for 3 Solutions Architects to lead technical engagements across its growing AI infrastructure practice. This is an integral pre-sales role that sits between technical depth and commercial delivery translating complex customer challenges into scalable, production-ready AI infrastructure solutions.
You will work on large-scale GPU deployments, distributed training and inference architectures, high-performance networking fabrics, and the software stack that makes it all run. The business has the relationships, credentials, and pipeline to give this hire immediate traction.
You’ll partner closely with account teams throughout the full sales cycle. From initial technical discovery and solution design through to proof-of-concept execution and handoff to delivery, you will be the technical authority that builds customer confidence and moves deals forward.
This role requires equal parts technical depth and commercial acumen. You will need to be as comfortable presenting ROI trade-offs and risk summaries to a CTO as you are discussing GPU memory bandwidth constraints or RDMA topology with an infrastructure engineering team.
Key Responsibilities
Technical Discovery & Qualification
* Run structured discovery to understand AI workload goals (training vs. inference), constraints (power, space, procurement, security), and customer success criteria
* Qualify opportunities early and advise account teams on architecture direction, risk, and deal strategy
Solution Architecture & Design
* Design end-to-end AI infrastructure architectures spanning GPU-accelerated compute, networking (Ethernet/InfiniBand/RoCE), storage, and the full software stack
* Produce practical sizing guidance, resilience models, and operability considerations aligned to real customer workloads
Proposals & Technical Documentation
* Author and review technical proposal content: architecture diagrams, BOM guidance, SoW inputs, risk/mitigation summaries, and acceptance criteria
* Produce documentation that communicates clearly to both technical practitioners and executive stakeholders
POC & Pilot Execution
* Plan and execute proof-of-concept and pilot activities with defined test plans, benchmark methodology, and success metrics
* Troubleshoot issues in collaboration with engineering and OEM/partner teams; document and communicate outcomes
Competitive Positioning & Enablement
* Contribute to reference architectures, competitive battlecards, and sales plays
* Help account teams articulate differentiation on performance, TCO, time-to-value, and supportability in competitive situations
Deal Governance & Delivery Handoff
* Ensure clean transitions from pre-sales to implementation with validated designs, clear requirements, and agreed acceptance criteria
* Support RFP/RFI responses, architecture reviews, and stakeholder alignment activities throughout the sales cycle
What We’re Looking For
Essential
* Proven experience in a customer-facing technical role supporting complex infrastructure or platform deals
* Strong technical literacy across enterprise and AI infrastructure: GPU servers, PCIe/NVLink-class interconnects, networking fabrics (Ethernet, InfiniBand, RoCE), storage fundamentals, and Linux-based operations
* Ability to translate workload and business goals into practical architectures with clear assumptions, trade-offs, and sizing across performance, cost, power, reliability, and security
* Consultative engagement skills: able to lead technical conversations, handle objections, and partner with account teams to progress and close deals
* Comfortable coordinating across engineering, delivery, and OEM/partner teams without direct authority
* Genuine curiosity about AI infrastructure: stays current on accelerator platforms, model trends, and the relationship between infrastructure choices and training/inference performance
Beneficial
* Hands-on experience with AI/HPC orchestration platforms (Kubernetes, Slurm, Ray) and familiarity with GPU software stacks CUDA ecosystem, container runtimes, driver management, libraries
* Knowledge of networking for distributed AI workloads: RDMA/RoCE, topology awareness, congestion management, and observability
* Experience producing pre-sales assets: reference architectures, sizing guides, benchmark reports, TCO/ROI models, and competitive battlecards
* Exposure to enterprise buying requirements: security/compliance, on-premises deployment constraints, procurement processes, and hardware lifecycle/lead-time realities
* Relevant vendor or industry certifications are a bonus