Specialist GenAI Solution Architect, AWS Specialist & Partner Industries Organization (ASPI), Global Financial Services (GFS)

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 worlds 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 wont 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 & AgentOps 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
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- 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 PyTorch or TensorFlow Hugging Face LLM serving infrastructure and strong familiarity with agentic frameworks (Strands LangGraph 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 AgentCore SageMaker) 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

- 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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