Key responsibilities

• Design and build AI-enabled applications for DB Intelligence, including user-facing workflows, APIs, microservices, orchestration components and data integration layers.
• Develop modern React / TypeScript front ends with reusable components, strong API integration and user experiences that support explainable AI-assisted workflows or
• Build Python services for AI/ML integration, model orchestration, RAG, agentic workflows, data processing, evaluation pipelines and automation or
• Develop and integrate Java / Spring Boot microservices for enterprise backend capabilities, business rules, workflow orchestration and secure service-to-service communication.
• Work with structured and unstructured data sources, including internal systems, documents, market/event data, portfolio data and enterprise knowledge sources.
• Contribute to AI architecture 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 contributions, design leadership, mentoring and reusable engineering patterns across the programs.

Required experience

• Significant professional software engineering experience, including recent hands-on delivery of AI, data, analytics or decision-support platforms.
• Strong React and modern front-end engineering experience, including TypeScript / JavaScript, state management, reusable component design and responsive UI development.
• Strong Python engineering experience, ideally including FastAPI / Flask, Pandas, data pipelines, AI/ML libraries, LLM integration, model evaluation or automation frameworks.
• Strong Java engineering experience, preferably with Spring Boot, REST APIs, microservices, event-driven architecture, resilience and enterprise integration patterns.
• 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