Learn Client Leadership: Observe and participate in high-level client discussions, learning how to present ideas, manage expectations, and solve complex business challenges.
To apply, please send your cover letter, CV and names of 3 references (name, institution, email) to the NUS Career page with a copy to Prof Lim Chwee Teck at [email protected]. Only shortlisted candidates will be contacted. Lab Website: https://ctlimlab.org/
Integrate AI Agents into daily R&D workflows and explore their applications in scenarios such as coding assistance, intelligent debugging, automated testing, issue troubleshooting, and business process automation.
Stay up to date with the latest advancements in Large Language Models (LLMs) and AIGC technologies, explore innovative quality and engineering efficiency solutions for software testing, and continuously refine them through practical application.
Stay up to date with the latest advancements in Large Language Models (LLMs) and AIGC technologies, explore innovative quality and engineering efficiency solutions for software testing, and continuously refine them through practical application.
SOP Development & Agent Skill Authorship - Translate frontline risk experience into structured SOPs and agent skill sets. Identify blind spots in current detection logic, document edge cases, and collaborate with the Product team to continuously improve automated coverage.
Secure Architecture & Policy Enforcement: Integrate fine-grained authorization and data access control frameworks leveraging Cedar, ensuring alignment with OSI and ODSC guidelines in restricted public sector infrastructure.
Leverage data to manage performance: monitor dashboards, detect anomalies, and perform data and root cause analysis to maintain quality and efficiency.
Data Analysis & Insights: Analyse user behaviour and operational data to detect patterns, inform decision-making, and optimise areas such as recommendations, personalisation, or process automation
Guideline Application: Apply complex policy documents to "edge cases"—scenarios where the answer isn't clearly right or wrong—to ensure consistent model alignment.