Key responsibilities

• Design and build AI-enabled applications for DB Intelligence, including user-facing workflows, APIs, microservices, orchestration components and data integration layers.
• Build Python services for AI/ML integration, model orchestration, RAG, agentic workflows, data processing, evaluation pipelines and automation.
• Work with structured and unstructured data sources, including internal systems, documents, market/event data, portfolio data and enterprise knowledge sources.
• Contribute to AI architectures using LLM APIs, prompt orchestration, embeddings, vector search, RAG, model evaluation, guardrails and human-in-the-loop review.
• Engineer solutions that support scenario analysis, event-driven intelligence, impact assessment, portfolio/risk insight generation and decision support.
• Apply strong engineering discipline: clean code, automated testing, CI/CD, code reviews, observability, performance tuning, resilience and production support readiness.
• Implement controls for data privacy, entitlement management, audit logging, explainability, traceability, model output monitoring and responsible AI usage.
• Provide senior technical contribution, design leadership, mentoring and reusable engineering patterns across the programme.

Required experience

• Significant professional software engineering experience, including recent hands-on delivery of AI, data, analytics or decision-support platforms..
• Strong Python engineering experience, ideally including FastAPI / Flask, Pandas, data pipelines, AI/ML libraries, LLM integration, model evaluation or automation frameworks.
• Experience building production-grade applications with clear understanding of security, scalability, availability, latency, observability and maintainability.
• Practical GenAI / AI application experience, such as LLM APIs, prompt engineering, embeddings, vector databases, RAG, semantic search, agent workflows, hallucination mitigation and guardrails.
• Experience integrating enterprise data sources and APIs, including SQL databases, document stores, search platforms, messaging/event platforms or data lakes.
• Strong understanding of secure engineering, authentication/authorisation, entitlement models, data protection and audit requirements.
• Experience working in Agile delivery teams and communicating complex technical concepts to technical and non-technical stakeholders.

Financial services / banking experience

Financial services experience is highly valuable, particularly in investment banking, corporate banking, risk, markets, research, KYC, credit, portfolio analytics or regulatory technology. Candidates should understand, or quickly adapt to, regulated banking environments with data sensitivity, operational resilience, model risk, access controls, evidence-based decisioning and governance expectations.