Familiarize yourself with the use of Large Language Models (LLMs) with agent systems, MCP interfaces, Retrieval-Augmented Generation (RAG), and related technologies
Develop and implement prototype applications to support agricultural processes using LLMs
Support the development of AI tools that make our engineers' daily work easier, accelerate development cycles, and further improve the quality of our machines and software
Analyze and process large amounts of unstructured data and integrate findings into knowledge bases
Validate and optimize models in the context of agricultural engineering and harvesting machinery
Collaborate with interdisciplinary research and development teams
Document results and present your work to the team

Currently enrolled in a degree program in Computer Science, Data Science, Artificial Intelligence, or a related field
Strong knowledge of Python and ideally experience with ML frameworks such as PyTorch, Ollama or Transformers, and with LangChain, LangGraph and optionally Neo4j
Interest in and some experience with Large Language Models (e.g., GPT, BERT, Llama) and modern NLP technologies
Experience with or interest in retrieval techniques and knowledge management (RAG, vector databases)
Analytical thinking, creativity, teamwork, initiative, and strong communication skills
Interest in applications in agriculture and technological innovation

Regular weekly working hours for the internship are 35 hours
Internship duration is 3–6 months
Opportunity to write a thesis in cooperation with CLAAS
Participation in the regular “CLAAS StudentTalk” event to network with students and employees

Internship, Thesis – Large Language Models (LLMs), RAG

Key skills