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