GenAI and Agentic AI are reshaping how financial institutions operate — from how they serve clients to how they manage risk, compliance, and internal operations. This role is about making that real.

As a Specialist GenAI Solution Architect covering Global Financial Services (GFS), you will operate as a trusted technical advisor to CTOs, Chief AI Officers, and engineering leadership at tier-one banks, insurers, and capital markets firms across EMEA and APJ — helping them navigate the architecture, governance, and operational decisions required to move agentic AI from experimentation into production at institutional scale.

GFS partners with a focused set of the world's most important financial institutions. As these organisations move from experimentation to production with foundation models, LLMs, and agentic systems, this role is about going deep with strategic customers — translating AI ambition into viable architectures, defensible technical strategies, and production-ready programmes that meet the demands of highly regulated environments.

This is a consultative, disruptive role. You will challenge conventional thinking, introduce new possibilities, and shape how major financial institutions adopt agentic AI.

• Key job responsibilities
• Agentic AI Architecture for Financial Services: Design multi-agent architectures that solve real financial services problems — claims automation, credit decisioning, regulatory reporting, client advisory — and define the path from prototype to production-grade deployment in regulated environments.
• Strategic Technical Advisory: Be the primary AI architecture partner to a focused set of strategic accounts — engaging CTO, CAIO, and engineering leadership to understand their business deeply, shape their AI roadmaps, and challenge assumptions. This is a consultative role. You are there to push thinking, not take orders.
• Full-Stack AI Architecture: Design end-to-end system architectures spanning model serving (vLLM/SGLang/TGI), frontend interaction patterns, API orchestration, and data integration — within constraints that satisfy financial services security, data residency, and auditability requirements. You won't be writing production code, but you need the depth to make credible, opinionated architecture calls and guide customer engineering teams through implementation.
• Governance & Security in Regulated Environments: Design and advise on Human-in-the-Loop protocols, explainability frameworks, audit trails, and model risk management practices aligned with regulatory expectations (MAS, PRA, FCA, EBA, APRA).
• Performance Optimisation: Advise on the trade-offs between model quality, latency, throughput, and token efficiency — particularly within the cost and performance constraints of real-time financial workflows.
• MLOps & Agent Ops Strategy: Guide customers on CI/CD pipeline design, automated testing for non-deterministic outputs, model versioning, and observability (tracing, drift detection) — establishing patterns that satisfy model risk management and internal audit expectations.
• Legacy & Modern System Integration: Design integration patterns that connect GenAI components with existing core banking/insurance platforms, ensuring data consistency across vector stores, graph databases, RDBMS, and legacy middleware.
• Reusable Technical Assets: Build reference architectures, design patterns, and proof-of-concept frameworks that scale what works from one institution to many across the GFS organisation.
• Technical Leadership & Enablement: Run architecture deep dives, technical workshops, and executive briefings — translating complex AI concepts into clear, actionable strategies for both technical and non-technical audiences.

About the team
Diverse Experiences
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Work/Life Balance
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Basic Qualifications

• Experience architecting or leading the delivery of AI/ML systems at scale within or for financial services organisations
• Deep understanding of modern AI/ML stacks — Py Torch or Tensor Flow, Hugging Face, LLM serving infrastructure — and strong familiarity with agentic frameworks (Strands, Lang Graph, CrewAI) and how they manage complex, stateful workflows
• Strong technical foundation in Python and at least one of Java, Go, or TypeScript, with deep understanding of modern backend architectures — enough to make credible architecture decisions and guide engineering teams, not necessarily write production code daily
• Hands-on experience with AWS AI/ML services (Bedrock, Agent Core, Sage Maker), including designing secure, private-network AI environments and RAG patterns (embeddings, vector stores, semantic search)
• Demonstrated ability to engage and influence senior technical stakeholders (CTO, CAIO, VP Engineering) in complex, regulated enterprise environments

Preferred Qualifications

• Direct domain experience in banking, insurance, or capital markets — understanding the workflows, risk frameworks, and regulatory landscape (MAS, PRA, FCA, EBA, APRA)
• Track record defining LLMOps practices — evaluation frameworks for non-deterministic outputs, reasoning traceability, drift detection
• Prior experience in a Solution Architecture, Sales Engineering, or CTO advisory capacity

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