01Overview
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 & 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.
Mindset & problem-solving
Big-picture, structured thinker frames the problem, sees the whole system, and reasons from first
principles under ambiguity.
Innovative solutioner brings fresh architectural thinking and options, challenges the obvious answer,
and balances vision with whats deliverable.
Pragmatic connects strategy to execution; makes defensible trade-off decisions and can justify them
to technical and business audiences.
Naturally curious about new AI capabilities and how to apply them to real business value.
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. .
