About the role:

Own the technical shape of AI-native engagements for financial institutions designing solutions and target architectures across discovery, new capability builds, and modernisation. Youll bring together enterprise architecture and applied GenAI to solve high-value, ambiguous problems.

Roles and Responsibilities:

- Lead solution and target architecture across engagements from greenfield AI-native builds to assessing and modernising complex existing systems.

- Run discovery: understand and document the architecture, functions, data and integrations of complex enterprise systems.

- Define improvement areas, new capabilities, target-state options and phased roadmaps with clear trade-offs.

- Design AI-augmented solutions (GenAI, RAG, agents) and shape how theyre applied to each problem.

- Model business / domain knowledge and align to industry standards where relevant.

- Own test strategy, quality and non-functional concerns; address governance and regulatory requirements.

- Lead SME and client-stakeholder sessions; guide and unblock the development team.

Must-have:

- Proven solution / enterprise architecture in financial services.

- Breadth across solution design, new builds and modernisation target-state design, roadmaps, trade-off analysis.

- Working knowledge of GenAI / RAG / agentic patterns enough to direct delivery.

- On-prem and cloud architecture; security and regulatory awareness for regulated FIs (DORA / FCA / EBA).

- Strong communication, facilitation and documentation.

Nice-to-have:

- Domain / knowledge modelling; standards (FIBO / BIAN / ISO 20022 / TOGAF).

- Test strategy / quality-engineering depth.

- Prior experience with AI-assisted analysis, transformation, or delivery accelerators.

- Awareness of semantic code-analysis / coding-agent tooling (e.g. Serena, LSP- / MCP-based) and how it accelerates legacy comprehension.

- Wealth-management / core-banking / book-keeping domain.