Research Engineer I (Trust Technologies)
Nanyang Technological University- Posted an hour ago
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Job Description
Nanyang Technological University's National Centre for Research in Digital Trust (DTC) is a Trust Technology Research Centre to execute a national program to help put Singapore into a strong trust hub. The key objective is to support efforts to create a trusted digital environment for its people and businesses by providing businesses and consumers with greater assurance and confidence as they digitalize.
We are looking for a Research Engineerto design, development and deployment of LLM-powered AI agents that transform core insurance workflows, including underwriting, claims, policy servicing, risk assessment and customer engagement. The role will build autonomous and multi-agent systems capable of interpreting insurance documents, applying policy rules, supporting decision-making and executing end-to-end processes, while ensuring reliability, compliance and human oversight. By combining large language models, agentic AI frameworks and insuranc domain knowledge, this position will help the business improve operational efficiency, decision quality, customer experience and responsible AI adoption across the insurance value chain.
Key Responsibilities:
Ability to code-switch effortlessly between talking business and talking tech, and can help stakeholders, engineers, and businesses easily understand complex AI and LLM research concepts
Conduct research into LLM-powered AI agents and multi-agent systems - translating algorithms, tools, and frameworks into working prototypes that can explain how research outputs can be productised into new insurance capabilities.
Work closely with Centre's researchers to design and develop system implementation work from research into the product, supporting key insurance workflows such as underwriting, claims, policy servicing and risk assessment.
Design and build working agent tools and evaluation frameworks that can support the technology transfer of new AI capabilities to research partners and can be used to showcase the value of LLM-based agentic AI in insurance.
Write and maintain technical documentation, presentations, and papers on AI agent design, evaluation, guardrails and trust mechanisms, helping to educate and raise the overall competency in responsible AI and trust technologies for insurance applications.
Engage global partners and researchers to understand latest trends in LLMs, agentic AI and insurance technology, and advance Singapore's mindshare in trustworthy AI agent systems for the insurance sector.
Job Requirements:
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering or a related discipline Relevant LLM or AI agent research experience is preferred.
Ability to code-switch effortlessly between talking business and talking tech, and can help stakeholders, engineers, and businesses easily understand complex AI and LLM research concepts
Strong hands-on experience with large language models, prompt engineering, retrieval-augmented generation, agent frameworks and multi-agent system design
Proficiency in Python and common AI/ML tools and frameworks, with experience building production-grade AI applications, APIs, evaluation pipelines or agent orchestration systems
Solid understanding of AI safety, guardrails, model evaluation, monitoring, data privacy, explainability and responsible AI practices in regulated environments
Good knowledge of system platform development, database technologies, and software engineering best practices
Experience in insurance, financial services or other regulated domains is preferred, including familiarity with underwriting, claims, policy servicing, risk assessment or customer engagement workflows
We regret that only shortlisted candidates will be notified.
More Info
Key Skills
software engineering best practices
production-grade AI applications
explainability
AI safety guardrails
retrieval-augmented generation
agent orchestration systems
agent frameworks
AI/ML tools and frameworks
prompt engineering
model evaluation
system platform development
evaluation pipelines
large language models
multi-agent system design
responsible AI practices
