Lead Research Engineer, ARTC

Job Responsibilities

Agentic AI for Demand Planning

Design and develop  agentic AI solutions for demand planning, integrating demand forecasting, demand sensing, supply-demand analysis, and planning recommendations.

Develop  LLM-powered agents and agentic workflows  that reason over supply chain data, invoke analytical tools and models, and generate actionable recommendations.

Apply recent  LLM and agentic AI technologies, including tool/function calling, RAG, structured outputs, context management, agent orchestration, and human-in-the-loop workflows.

Integrate LLM agents with  forecasting models, optimization algorithms, enterprise data, APIs, and business rules  to support end-to-end planning processes.

Demand Planning Analytics

Develop and enhance  demand forecasting and predictive analytics models, including data preparation, feature engineering, model evaluation, and forecast accuracy analysis.

Analyze demand changes, forecast deviations, supply-demand imbalances, and  material shortage risks  and assess their downstream impact.

Develop data-driven  purchase and mitigation recommendations  based on demand, inventory, supply availability, lead times, and operational constraints.

AI Application Development

Develop backend services and APIs integrating  LLMs, AI agents, analytical models, databases, and enterprise systems.

Develop and integrate  interactive frontends and dashboards  that enable planners to review forecasts, investigate demand changes, interact with AI agents, and evaluate recommended actions.

Build end-to-end prototypes connecting  frontend applications, backend services, agentic workflows, analytical models, and enterprise data.

Implement appropriate validation, monitoring, and  human-in-the-loop controls  to improve the reliability and explainability of AI-generated recommendations.

Research & Collaboration

Evaluate and apply emerging developments in  LLMs, agentic AI, multi-agent systems, and AI application architectures  to supply chain use cases.

Work closely with supply chain experts, AI scientists, data engineers, and software developers to translate business challenges into  practical AI-driven solutions.

Contribute to research and industry projects in  agentic demand planning and AI-driven supply chain decision support.

Job Requirements

Bachelor’s/Master’s degree in  Computer Science, Data Science, Artificial Intelligence, Industrial Engineering, Operations Research, Supply Chain Management, or a related field

Strong proficiency in  Python  and relevant data analytics/AI libraries such as Pandas, NumPy, Scikit-learn, PyTorch, or equivalent frameworks

Hands-on experience developing applications using  recent Large Language Model (LLM) architectures and technologies, including prompt engineering, structured outputs, tool/function calling, retrieval-augmented generation (RAG), and context management

Practical experience designing and implementing  agentic AI or multi-agent workflows, including agent orchestration, tool integration, planning/reasoning, workflow management, and human-in-the-loop mechanisms

Familiarity with modern  LLM and agentic application frameworks  such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, or equivalent technologies

Experience integrating LLMs and AI agents with  analytical models, databases, APIs, enterprise systems, and external tools  to build end-to-end AI applications

Strong  backend development  experience, including development of REST APIs, microservices, data services, and application/business logic using frameworks such as FastAPI, Flask, or equivalent technologies

Experience in  frontend development and integration  for AI applications using technologies such as React, Streamlit, Dash, Plotly, PyQt, or equivalent frameworks

Ability to develop  end-to-end prototypes, connecting frontend interfaces, backend services, LLM/agentic workflows, analytical models, databases, and enterprise systems

Relevant experience in  machine learning and predictive analytics, preferably involving time-series forecasting, demand forecasting, optimization, or other supply chain applications

Understanding of  demand planning and supply chain analytics, including demand signals, forecast accuracy, inventory, material requirements, supply-demand balancing, and related supply chain KPIs

Familiarity with enterprise and supply chain systems such as  SAP, ERP, MRP, MES, or related planning and transactional data sources is highly preferred

Experience implementing appropriate  LLM guardrails, output validation, observability, evaluation, and monitoring  to improve the reliability and traceability of agentic AI applications

Strong analytical and problem-solving skills, with the ability to translate complex business and planning problems into  AI-enabled workflows and practical decision-support solutions

Excellent interpersonal and communication skills with the ability to work effectively with  supply chain domain experts, AI researchers, data engineers, software developers, and industry stakeholders

Bonus: Familiarity with  LLM evaluation, reasoning models, MCP/tool integration, vector databases, knowledge graphs, agent memory, workflow automation, explainable AI, and cloud/container deployment  is advantageous

The above eligibility criteria are not exhaustive. A*STAR may include additional selection criteria based on its prevailing recruitment policies. These policies may be amended from time to time without notice. We regret that only shortlisted candidates will be notified.