Artificial Intelligence Engineer/Researcher

Client of United Nation careers

Background: The United Nations Economic and Social Commission for Western Asia (ESCWA) is mandated to support the sustainable development of the Arab region through evidence-based policymaking, data analytics, and digital innovation. The Decision-Support and Data Science Division (DSDS) leads ESCWA's efforts in developing advanced analytical tools and AI-powered platforms to assist member states in formulating effective public policies. Agent-Based Modelling (ABM) constitutes one of the methodologies used for simulating complex socioeconomic systems, enabling policymakers to assess the potential impact of interventions in non-linear, dynamic environments. The rapid advancement of Large Language Models (LLMs) presents a transformative opportunity to augment ABM frameworks with natural language reasoning, contextual intelligence, and adaptive policy advisory capabilities. In this context, DSDS seeks to hire a junior AI Engineer/Researcher to undertake a rigorous, phased research and development initiative integrating LLMs into ABM architectures. The outputs of this initiative will directly contribute to ESCWA's AI for Policy program and will yield both publications and deployable decision-support prototypes for use by ESCWA member states.

Duties and responsibilities: Under the overall supervision of the Chief of the Decision-Support and Data Science Division (DSDS), and in close coordination with the designated ESCWA focal point, the Consultant shall carry out the following tasks:

Phase 1: ABM Calibration and Validation (June July 2026)

• Improve calibration performance and robustness of the existing ABM model against current data targets for the rule-based ABM model.
• Run comprehensive scalability tests across number of agents, simulation steps, calibration iterations, and simulation runs.
• Define a historical policy test set, target data, and validation metrics in alignment with DSDS standards.
• Simulate emerging policy scenarios, analyze results, and produce policy-relevant insights.
• Prepare and submit a draft report on the findings

Phase 2: Literature Review and Architecture Design (August September 2026)

• Conduct a comprehensive and systematic literature review covering the ABM+LLM research landscape, including key methodologies, benchmarks, and applications.
• Define the conceptual architecture for ABM+LLM integration, specifying the roles of LLMs as contextualizers and policy advisors embedded within the ABM framework.
• Document the architecture, methodology, and a concrete case study relevant to Arab regional development contexts as assigned by Chief DSDS

Phase 3: LLM Integration and Benchmarking (October November 2026)

• Develop and deploy the first version of the LLM-enhanced ABM model, integrating LLMs as analytical tools within the simulation environment.
• Conduct rigorous benchmarking of the LLM-enhanced ABM against established LLM-based decision-support approaches, including standard LLM, RAG-based LLM, and MAS LLM configurations.
• Integrate LLMs as autonomous agents and share the model with assigned domain experts for participatory validation and human-in-the-loop refinement.
• Deploy the first version of the LLM-empowered ABM model to the DSDS GitHub repository.