Research Engineer, AIDF

About the company

Join AIDF at NUS to turn applied AI research into reliable enterprise products for financial institutions. You will develop full stack systems and AI agents, bring them into production, and improve them through testing and client feedback.

Your tasks

This role combines hands-on engineering with independent technical judgment. You will work with researchers, engineers, product teams, vendors, and clients to deliver secure, maintainable solutions, balancing quality, speed, and cost throughout the product lifecycle.

Explore FinTutor, one of our AI research projects.

• Deliver enterprise systems. Own features across frontend, backend, data, and integration layers. Translate business needs into practical designs and take solutions through testing, deployment, acceptance, and ongoing support in enterprise environments.
• Engineer and direct AI agents. Design and refine agent workflows, retrieval-augmented generation (RAG), and guardrails. Apply technical expertise to guide AI behaviour, challenge outputs, diagnose failures, and meet measurable quality targets.
• Establish production readiness. Build reliable release and operational practices, including automated testing, controlled deployment, monitoring, and recovery. Resolve production issues and ensure systems remain secure, observable, and maintainable after launch.
• Validate application and AI security. Plan and conduct vulnerability assessment and penetration testing (VAPT) and LLM security testing, working with specialists where needed. Assess risks such as prompt injection, data leakage, and unsafe tool use; remediate findings and verify fixes.
• Coordinate vendors and delivery partners. Evaluate technical fit, integration requirements, service quality, and cost. Align responsibilities and acceptance criteria, manage dependencies, and drive resolution of technical and delivery issues across external providers.
• Communicate across technical and business audiences. Explain system behaviour, architecture, risks, and trade-offs clearly to product teams, management, and clients. Turn discussions into agreed requirements and actionable decisions, and surface constraints early.
• Iterate quickly and use resources wisely. Prioritise high-impact work, shorten feedback cycles, and validate improvements with evidence. Optimise model, infrastructure, and operating costs while maintaining required quality, security, and performance.
• Translate research into practical improvements. Assess emerging AI methods and tools, test their relevance to real business problems, and integrate useful advances. Document

decisions and results so others can operate and extend the work.

Requirements

• Education. A master's degree or above in Computer Science, Artificial Intelligence, Software Engineering, Data Science, or a related discipline.
• Strong engineering foundations. Practical full stack development experience and sound knowledge of software architecture, APIs, databases, and system integration. Be able to reason through design choices, review code critically, and solve unfamiliar technical problems.
• Enterprise delivery experience. Demonstrated ability to deploy and support applications in production, including cloud or enterprise infrastructure, release processes, access controls, and operational troubleshooting. Understand how to move a prototype into dependable use.
• AI expertise and independent judgment. Hands-on experience with LLM applications, agent orchestration, RAG, and evaluation. Use technical depth to guide and assess AI-generated work, identify limitations, and justify independent decisions.
• Security testing capability. Practical knowledge of VAPT and LLM security testing, from planning and findings analysis to remediation and verification. Understand application and AI security risks, assess controls, and collaborate effectively with security specialists.
• Stakeholder and vendor collaboration. Ability to work constructively with external providers and communicate complex technical topics in clear, accessible language. Adapt explanations to engineers, product colleagues, management, and customers, linking technical choices to business implications.
• Ownership and learning agility. Take responsibility for outcomes, follow through on commitments, and address risks proactively. Learn quickly, stay current with AI developments, and test new ideas critically. Work efficiently and iterate rapidly with sound judgment about priorities, effort, and cost.

Additional Strengths

• Financial knowledge is a plus. Familiarity with financial products, institutional workflows, or financial services would help you understand client needs and design relevant solutions.

What we offer

• Research and engineering collaboration. Work with AIDF researchers and experienced engineers to connect applied AI research with production systems.
• Direct industry exposure. Engage with financial institution clients, understand real businesschallenges, and contribute to practical solutions.
• Meaningful product ownership. Influence technical decisions and see your work through deployment, client feedback, and continued improvement.

How to apply

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.

NUS Career Portal link - https://careers.nus.edu.sg/job/Research-Engineer%2C-AIDF/34688-en_GB/?st=380D401FFA7C4302B35EE7D6DDDBB0F778220D0F

Send your CV, relevant project or portfolio links, and availability. Briefly describe a system you helped deliver, your own contribution, and a technical challenge you resolved. You may use a non-confidential project summary where code cannot be shared.