AI Solution Architect - #2192511

Greetings

We are Hiring AI Solution Architect - FDT in Sheffield, UK

3 days onsite

6 Months contract

Candidate should undergo karat assessment

AI Solution Architect - FDT

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.

AI pattern selection with explicit trade-offs (ML/GenAI/hybrid)

Interfaces and integration contracts (APIs/events/data contracts) with fallbacks and error handling

Non-functional requirements and SLO-driven design (availability, latency, resilience, cost)

Controls-by-design (security, privacy, auditability) aligned to governance expectations

MLOps/LLMOps lifecycle design (versioning, deployment, monitoring, rollback, ownership)

Capability

Customer / Client Centricity

Engineering discipline & quality assurance

Leading simplification and automation at solution level

Judgment and decision-making

Influencing and stakeholder alignment

Championing innovation through rapid iteration

Strategic technical thinking

Inspirational Leadership

Talent stewardship