Master Thesis / Internship: Large Language Models and AI Agents in Supply Chain (f/m/div)

Infineon’s supply chain domain is a rich repository of diverse data, guidelines, onboarding materials, process documentation, and answers to frequently asked questions. Enabling efficient knowledge retrieval within this environment is essential to enhance decision-making, reduce response times, and improve operational efficiency. Traditional retrieval methods—such as Retrieval-Augmented Generation (RAG) and vector database-based systems—have dominated knowledge management for some time. However, with advancements in artificial intelligence, particularly large language models (LLMs), the potential for enhanced knowledge retrieval has expanded significantly. Models now offer larger context windows, improved reasoning capabilities, and tool-handling features like advanced search, paving the way for innovative solutions that go beyond conventional approaches.

Your Role
Key responsibilities in your new role
This thesis focuses on exploring and evaluating state-of-the-art methods for knowledge retrieval that use large language models in the context of supply chain management at Infineon. The goal is to establish a scalable approach for creating a central entry point for supply chain-related queries. By investigating novel methodologies and leveraging the latest advancements, the project aims to overcome existing challenges and fully realize the capabilities of modern AI.
Key objectives include investigating methods for efficient data-fetching, preprocessing, and context definition, while building and tailoring tools specifically designed to enhance AI agent performance. The project also aims to evaluate how standardized protocols like Model Context Protocols (MCP) can ensure scalability and seamless integration, demonstrating the capabilities of AI agents in handling real-world, data-intensive scenarios.

Specifically, the thesis should include the following objectives:

• Development of a framework for leveraging large language models in supply chain knowledge retrieval
• Design and implementation of a proof-of-concept system that serves as a central entry point for answering supply chain questions
• Exploration and documentation of alternative retrieval mechanisms, including reasoning-based approaches and tool-assisted queries using LLMs
• Detailed analysis of the performance, scalability, and limitations of the system compared to classical methods
• Recommendations for adopting these approaches in real-world supply chain environments at Infineon

Your Profile
Qualifications and skills to help you succeed

• Study Field: You are currently studying Management & Technology, FIM or TUM Information Systems with a major in the area of supply chain management
• Experience: You must have a strong analytical background, be able to work independently and must show absolute reliability and an interest in the methodological foundation of Agentic AI and its practical use cases in solving real-world problems
• Skills: Very good MS-Office skills (Word, Excel, PowerPoint) as well as good English communication and presentation skills are mandatory. Basic knowledge about Python programming is needed. Experiences with LLM is a clear plus and will be preferred
• Language: The thesis has to be conducted in English

Please attach the following documents to your application:

• CV in English
• Certificate of enrollment at university
• Excerpt of the study regulations for the mandatory internship (if applicable)
• Latest grades transcript (not older than 6 months)
• High school report

Contact:
    Rebekka Kohnle

Further links:

• Find out what we are looking for in your CV
• Find out how the student application process works with us
• Discover our student website

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