Research Engineer/Assistant (Agentic AI & Urban Intelligence)-Cities Foresight Lab(CFL),NUS Cities

National University of Singapore; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

The National University of Singapore is seeking a Research Engineer/Assistant to join the Cities Foresight Lab. This role involves developing a framework that enhances urban policy and planning using AI-driven recommendations.

Responsibilities include designing a reasoning framework, implementing a Model Context Protocol, and collaborating with urban planners. Candidates should have a Bachelor's or Master's in a quantitative field and be proficient in Python. The position offers opportunities for interdisciplinary work and contributions to significant urban projects.

Qualifications

• Degree in Computer Science, AI, or Data Science with computational focus.
• Experience with agentic frameworks and system architecture.
• Familiarity with natural language processing tasks.

Responsibilities

• Develop and maintain a multi-step reasoning framework.
• Implement Model Context Protocol servers for data standardization.
• Perform data synthesis for urban intelligence case studies.
• Define and track system performance and user validation metrics.
• Contribute to data/method documentation and publications.

Skills

Proficiency in Python

Critical thinking

Communication skills

Data engineering

Education

Bachelor's or Master's Degree in Computer Science or related field

Tools

LangGraph

GeoPandas

Shapely

Job description

Job Title

Research Engineer/Assistant (Agentic AI & Urban Intelligence) – Cities Foresight Lab (CFL), NUS Cities

University-Level Unit

College of Design and Engineering

Faculty/Department-Level Unit

Architecture

Employee Category

Research Staff

Location

Kent Ridge Campus

Posting Start Date

28/04/2026

Job Description

NUS Cities Foresight Lab (CFL) is seeking a Research Engineer/Assistant to develop an Agentic Orchestration Framework for Urban Intelligence. The project aims to demonstrate how agentic systems can enhance efficiency and consistency in policy and planning workflows by synthesizing unstructured datasets (e.g., text, social media, regulatory frameworks) with structured spatiotemporal information to generate responsive, data-driven recommendations.

You will be responsible for implementing a scalable Agentic Framework with Model Context Protocol (MCP) integration to connect diverse data sources with expert agent models. The role involves developing an orchestration layer capable of transforming complex, multi-scale technical inputs into actionable, reasoned insights through a natural-language interface. You will work alongside urban planning and social science researchers to ensure the system's outputs are interpretable, context-aware, and relevant to real-world policy and planning workflows.

Key Responsibilities

• Agentic Framework Design: develop and maintain a multi-step reasoning framework (e.g., LangGraph or similar) that can autonomously decompose high-level user objectives into executable tasks.
• MCP Integration: implement and scale Model Context Protocol servers to standardize the interface layer between external data repositories, real-time APIs, and specialized analytical models.
• Urban Intelligence Case Demonstration: perform data synthesis for case studies, spatiotemporal tool engineering, and expert agent tuning.
• Performance Evaluation: define and track system performance (e.g., API compatibility across system architecture, step tracing) and user validation metrics (e.g., ground‑truthing, benchmarking against manual workflows).
• Documentation & Publication: contribute to data/method documentation, visualisations, and writing reports/publications.

Qualifications

• Bachelor's or Master's Degree in Computer Science, Artificial Intelligence, Data Science, or a related quantitative field with a strong computational focus.
• Proficiency in Python.
• Hands‑on experience with agentic frameworks, specifically in designing multi-step reasoning loops and tool‑calling logic, and system architecture, including MCP.
• Experience in data engineering, including the ability to handle both unstructured and structured datasets.
• Familiarity with natural language processing tasks such as sentiment analysis, knowledge bases, and information retrieval.
• Resourceful and critical with good communication skills; able to work independently while collaborating effectively with interdisciplinary teams of urban planners and social scientists.

Preferred

• Experience in retrieval-augmented generation (RAG), knowledge graphs, or structured reasoning over heterogeneous data sources.
• Familiarity with cloud deployment environments and modern software development practices.
• Familiarity with geospatial data analysis and libraries (e.g., GeoPandas, Shapely, or equivalent).
• Familiarity with explainability and trust frameworks for AI systems, particularly in public sector or governance contexts.
• Interest in urban, social, or policy applications of data science and AI.