We are actively building diverse teams and welcome applications from everyone.
Role: Solutions Architect
Location: Birmingham (SCC operate hybrid working, which comprises of a mix of office and home working)
Contract Type: Permanent
Salary Package: competitive salary plus large company benefits, a broad flexible benefits scheme, and 2 paid-for volunteering days a year
Hours: 9.00 am – 5.30 pm Monday – Friday
Interview Process: 2-stage process
Why SCC?
- An inclusive workplace
- Excellent package: solid basic and company benefits
- Hybrid working & core hours in line with role requirements
- Career development and life-long learning opportunities
- Opportunity to join Europe's largest privately-owned IT Company
Role purpose: The
Pre-Sales Architect (AI Infrastructure) leads technical discovery, solution
design, and customer engagement for AI infrastructure opportunities—spanning
GPU-accelerated servers, networking/fabrics, storage, and the software stack
required to deploy and operate AI workloads (drivers, orchestration, cluster
management, observability, and lifecycle services). You partner with account
teams to qualify opportunities, shape requirements, create compelling
architectures and proposals, and guide proof-of-concept and pilot activities
through to a successful handoff for delivery. This role is equal parts
technical depth and commercial impact: translating customer outcomes into
feasible, supportable designs; articulating value, risk, and trade-offs; and
enabling customers to achieve performance, reliability, and cost targets in
real deployments. The role requires confidence in positioning secure,
resilient, and scalable AI‑ready enterprise architectures.
Key responsibilities:
- Opportunity Qualification & Technical Discovery — Run structured discovery with customers and internal stakeholders to clarify AI workload goals (training/inference), constraints (space/power, procurement, security), success criteria, and buying process.
- Solution Architecture & Design — Produce end-to-end AI infrastructure architectures across compute, networking, storage, and software; define assumptions, sizing, resilience, and operabil-ity for target workloads.
- Workload Sizing & Performance Guidance — Translate model/workload requirements into prac-tical sizing (GPU count, memory, interconnect, storage throughput), and explain performance trade-offs, scaling limits, and bottlenecks.
- Technical Proposal & Documentation — Create and review technical sections of proposals (SoW inputs, architecture diagrams/description, BOM guidance, risks/mitigations, acceptance criteria, and deployment approach) tailored to customer stakeholders.
- POCs, Pilots & Benchmarks — Plan and execute proof-of-concept and pilot activities (test plans, success metrics, benchmark methodology), troubleshoot issues with engineering/partners, and document outcomes.
- Customer Communication & Executive Readouts — Present architectures, value propositions, and trade-offs to technical and executive audiences; facilitate design reviews and decision check-points.
- Competitive Positioning & Field Enablement — Contribute to battlecards, reference architec-tures, and sales plays; help teams articulate differentiation (performance, time-to-value, TCO, supportability) in competitive deals.
- Handoff to Delivery & Customer Success — Ensure a clean transition from pre-sales to imple-mentation with clear requirements, validated design, risks, and acceptance tests; stay engaged through early deployment to unblock issues.
- Deal Support & Governance — Support RFP/RFI responses, security and architecture reviews, and stakeholder alignment; maintain opportunity notes, solution assumptions, and decision logs to reduce execution risk.
Skills and experience:
Required
- Experience in a customer-facing technical role (pre-sales architect, solutions architect, sales engi-neer, field engineer, technical consultant) supporting complex infrastructure or platform deals.
- Strong technical literacy across enterprise / AI infrastructure: GPU servers, interconnect con-cepts (PCIe/NVLink-class), networking fabrics (Ethernet/InfiniBand/RoCE), storage fundamentals, and Linux-based operations.
- Ability to translate business and workload goals into architectures, sizing, and trade-offs (per-formance, cost, power, reliability, security, and time-to-deploy) with clear assumptions and ac-ceptance criteria.
- Consultative selling skills: comfortable leading technical conversations, influencing stakeholders, handling objections, and partnering with account teams to progress deals.
- Strong communication: can write high-quality solution documentation and present to both technical audiences (platform/infra teams) and executives (outcomes, risk, ROI/TCO, timelines).
- Experience coordinating across engineering, delivery, and partners/OEMs; able to drive align-ment without authority and keep multiple workstreams moving during sales cycles.
- Curiosity and continuous learning in AI: keeping current on accelerator platforms, model trends, and how infrastructure choices impact training/inference performance and total cost of owner-ship.
Preferred
- Hands-on experience with AI/HPC platforms (Kubernetes, Slurm, Ray) and familiarity with GPU software stacks (CUDA ecosystem concepts, drivers, container runtimes, libraries).
- Knowledge of networking for distributed training/inference (RDMA/RoCE concepts, topology awareness, congestion management, observability) and data pipeline/storage considerations.
- Experience building and delivering pre-sales assets: reference architectures, sizing guides, benchmark reports, TCO/ROI models, competitive battlecards, and technical workshop material.
- Exposure to enterprise requirements: security/compliance, on-prem deployment constraints, procurement processes, lifecycle support, and supply/lead-time realities for hardware-led solu-tions.
- Industry standard qualifications desirable. Vendor specific qualifications desirable.