Solutions Architect - #2195663

Role description & core accountabilities

This role exists because high-impact AI delivery needs fast, explicit architecture decisions: which AI pattern to use, how it plugs into workflows, how to manage safety/security/controls, and how to operate and improve the solution after go-live.

The AI Solution Architect accelerates learning loops while maintaining architectural discipline, ensuring what’s designed can scale, be supported and be reused.

• End-to-end solution design (from problem to production) - Translate the use case into a target architecture: channels/UI, orchestration, AI services (ML/LLM), data stores, integrations, observability, and run operations. Define build vs buy, component selection, and non-functional requirements (availability, latency, cost, resilience).
• AI pattern selection and fit-for-purpose design - Choose the right AI approach and reference pattern: predictive ML (batch/real-time scoring), GenAI (RAG, summarisation, classification with LLMs, agentic workflows/tool use), and hybrid designs (rules + ML/LLM). Make the “why this approach” explicit: expected uplift, risk profile, operational complexity.
• Integration into the value stream - Design how AI outputs become decisions/actions in the workflow (human-in-the-loop vs straight-through processing). Define interfaces and contracts with upstream/downstream systems (APIs, events, data contracts), including error handling and fallbacks.
• Security, privacy, and controls-by-design - Ensure authentication/authorisation, data minimisation, encryption, logging, and secrets management are built in. Prevent data leakage and injection risks; ensure controlled tool access and safe prompt/context handling. Align solution to required governance (model approval, validation evidence, audit trail); coordinate with risk/compliance partners.
• MLOps/LLMOps and lifecycle management - Define deployment strategy, model/version management, prompt/versioning, and rollback approaches. Specify monitoring: model quality, drift, retrieval quality, safety metrics, latency, cost, and business KPIs. Define retraining/re-indexing cadence and ownership (RACI) post go-live.
• Architecture governance and technical leadership - Own architecture decisions (ADRs), ensure alignment to enterprise standards, and manage technical debt. Provide cross-squad reuse: reference architectures, templates, common components (e.g., evaluation harness, guardrails service).
• Delivery enablement - Break architecture into deliverable increments and help the Product Owner/Scrum team sequence work. Unblock engineers with clear designs, diagrams, interface specs, and decision logs.

Experience — Required

• Proven experience designing end-to-end solution architectures in enterprise environments, covering channels/UI, APIs/integration, data stores, observability and run operations.
• Strong ability to rapidly shape options with squads and users, then produce production-viable target architectures and incremental delivery plans.
• Experience selecting fit-for-purpose AI patterns (predictive ML, GenAI/RAG, hybrid) and articulating trade-offs (uplift vs risk vs operational complexity).
• Experience defining non-functional requirements (availability, latency, resilience, cost) and translating them into concrete design decisions and SLOs.
• Experience defining interfaces and contracts (APIs, events, data contracts), including error handling, fallbacks and degraded-mode behaviour.
• Working knowledge of security, privacy and controls-by-design practices (authentication/authorisation, encryption, secrets, logging, data minimisation) and how to embed them in architecture.
• Comfortable operating in ambiguous environments with shifting requirements, while maintaining delivery discipline and transparency.

Experience — Preferred

• Experience delivering within regulated/controlled environments (risk, audit, security, data handling) and navigating governance pragmatically (approval evidence, audit trails).
• Experience with GenAI architectures including RAG/agentic workflows, guardrails, grounding/citation patterns, and evaluation approaches.
• Experience with MLOps/LLMOps, including versioning, deployment strategies (canary/blue-green), monitoring, rollback and lifecycle ownership models.
• Experience contributing reusable reference architectures, templates and patterns across multiple squads/value streams.
• Experience working in Value Streams / product-led delivery, transformation pods, or embedded/forward-deployed models.