Shape the future of data and AI for the world's largest and most complex financial institutions. As a Specialist Data & AI Solutions Architect within our Global Financial Services (GFS) organisation, you'll work with a focused portfolio of strategic accounts across EMEA and APJ, designing the data foundations, governance frameworks, and AI-ready architectures that enable tier-one financial services customers to move from experimentation to production-grade agentic and generative AI systems at enterprise scale.
This is not a high-volume role. You'll go deep, not wide — building lasting technical relationships with a small number of the most significant financial services organisations in the world. You'll act as a strategic technical partner, helping these customers build the data infrastructure that underpins their AI ambitions: from unified data platforms and real-time feature stores through to the retrieval, grounding, and orchestration layers that agentic systems depend on.
You'll combine deep expertise in data engineering, analytics, and AI/ML with an understanding of the financial services landscape to translate complex challenges into elegant, scalable cloud solutions — navigating regulatory requirements, data sovereignty constraints, legacy environments, and multi-regional complexity along the way.
This role sits at the intersection of two accelerating trends: the modernisation of financial services data platforms, and the emergence of agentic AI systems that require those platforms to be well-governed, real-time, and semantically rich. You will help customers connect these two agendas into a single coherent strategy.
Key job responsibilities
- Serve as the trusted technical advisor for data and AI strategy across a small number of the largest, most complex Global Financial Services accounts in EMEA and APJ
- Design data architectures — lakehouses, knowledge graphs, vector stores, feature platforms, real-time pipelines — that form the foundation for agentic and generative AI, tailored to the regulatory, sovereignty, and resilience requirements of tier-one financial institutions
- Guide customers on building AI-ready data estates: quality, lineage, governance, semantic layers, and retrieval frameworks that enable trustworthy AI grounding at scale
- Advise on agentic system architecture — RAG, tool-use patterns, memory and state management, multi-agent orchestration, and human-in-the-loop guardrails — with attention to auditability, explainability, and compliance
- Collaborate with account teams, GenAI specialists, and the broader specialist SA community to shape long-term strategies connecting data modernisation to AI value realisation
- Engage at senior technical and executive levels, translating complex concepts into business-aligned recommendations that link data investment to measurable AI outcomes
- Develop reference architectures and reusable assets addressing financial services patterns — regulatory-compliant RAG, multi-jurisdictional data mesh, real-time decisioning, and secure agent-to-data connectivity
- Publish thought leadership content (whitepapers, blogs, workshops) on data foundations for AI and agentic system design in financial services
- Partner with service teams (Bedrock, Q, Glue, Lake Formation, Redshift, OpenSearch, Neptune) and product groups to influence roadmap priorities based on customer needs
- Stay current on emerging trends in data architecture, foundation models, and agentic AI frameworks, feeding insights into customer engagements and internal communities
A day in the life You'll join a focused, high-calibre group of specialist Solution Architects supporting the world's most important financial services customers. The team operates across regions on deeply technical, strategically significant engagements — solving problems that don't have off-the-shelf answers. Day-to-day, you'll design data platforms that underpin agentic AI workloads, review retrieval and grounding architectures, advise on governance models that satisfy regulators while enabling AI innovation, and help customers connect existing data investments to the AI outcomes their leadership demands. We value depth, intellectual curiosity, and a genuine desire to help customers succeed.
About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying.
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Basic Qualifications
- Master's degree or above in computer science, engineering, mathematics or equivalent, or Bachelor's degree in computer science, engineering, mathematics or equivalent
- Experience in IT development or implementation/consulting in the software or Internet industries
- Experience within specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics).
- Experience with data infrastructures: relational analytic DBMS, Elastic-Search, and Big Data EMR/EC2/Glue/Lambda, or experience with training and deploying machine learning systems to solve large-scale optimizations
- Experience communicating across technical and non-technical audiences, including executive level stakeholders or clients
Preferred Qualifications
- Experience working with Data & AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage
- PhD in computer science, machine learning, engineering, or related fields, or experience with AWS services including S3, Redshift, Sagemaker, EMR, Kinesis, Lambda, and EC2
- Knowledge of general AI tools
- Knowledge of data engineering pipelines, cloud solutions, ETL management, databases, visualizations and analytical platforms
- Experience with data analytics platforms (Power BI, Python, SQL, Tableau), or experience working with large-scale data mining and reporting tools (examples: SQL, MS Access, Essbase, Cognos) and other financial systems (examples: Oracle, SAP, Lawson, JD Edwards)
- Experience developing, deploying and managing AI products at scale
- Experience with data governance, lineage, and quality frameworks in regulated industries — particularly financial services
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