We are actively building diverse teams and welcome applications from everyone.
Role: Product Manager - ENT AI Infrastructure
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 Product Manager defines, build, and scale a portfolio of AI infrastructure and private AI solutions—covering GPU-accelerated servers, high-speed networking, storage, and the associated software stack that makes AI usable in production (deployment tooling, orchestration, observability, security, and lifecycle management). You’ll connect customer outcomes with technical feasibility, shaping roadmap, packaging, and go-to-market for secure, high-performance AI delivered on-premises or in private/hybrid environments.
Full UK driving licence needed.
Key responsibilities:
- Own product strategy and roadmap for AI infrastructure/private AI offerings, aligning customer needs, competitive landscape, and business goals.
- Develop deep customer understanding through discovery: target personas (IT, platform engineering, ML, security), top use cases, buying triggers, and constraints (data sovereignty, compliance, latency, cost, power/cooling).
- Translate AI workloads into product requirements (training vs. inference, model sizes, through-put/latency targets, scaling patterns, reliability and supportability).
- Define packaging and commercial models (bundles/SKUs, subscriptions, services attach, partner offers) that simplify buying and deployment.
- Partner with engineering and architecture to prioritise and deliver capabilities across compute, networking, storage, and software layers (orchestration, MLOps, monitoring, security).
- Write clear PRDs, user stories, and acceptance criteria; drive cross-functional execution with predictable delivery.
- Drive go-to-market readiness: positioning, messaging, sales enablement assets, demos, competitive plays, and launch plans.
- Establish product metrics and feedback loops (adoption, performance, reliability, NPS/customer satisfaction, attach rates) and iterate based on data.
- Work with partners (OEMs, GPU vendors, ISVs, SIs) to build validated reference architectures and integrated solutions.
- Ensure operational excellence: documentation, upgrade paths, lifecycle planning, support escalations, and continuous improvement.
Skills and experience required:
• Experience in product management or an adjacent role where you owned requirements and influenced roadmap (e.g., solutions architect, engineering lead, technical program manager, technical marketing, pre-sales).
• Ability to work cross-functionally and drive alignment across engineering, sales, partners, ser-vices, and support.
• Strong written and verbal communication: can turn complex infrastructure/AI concepts into crisp product narratives.
• Solid technical foundation in infrastructure and cloud-native concepts (compute, storage, net-working, containers/orchestration, security basics).
• Customer-centric mindset with hands-on discovery skills (interviews, problem framing, prioritisation).
• Passion for AI and accelerated computing, with evidence of continuous learning (projects, labs, community, courses, etc.).
Nice to have:
• Product experience in datacentre hardware, cloud infrastructure, HPC, AI platforms, or security/compliance products.
• Familiarity with GPU ecosystem concepts (CUDA awareness, GPU memory constraints, interconnects, training vs. inference economics).
• Understanding of private AI solution patterns and components (RAG, vector databases, model serving, evaluation, guardrails, observability).
• Experience defining reference architectures and deployment blueprints for on-premises and hybrid environments.
• Comfort with performance, reliability, and capacity planning topics (benchmarking, SLOs/SLAs, utilisation, power/cooling considerations).
• Partner ecosystem experience and experience launching joint solutions with OEMs/ISVs/SIs.