Doktorand Graph Intelligence & Agentic Graph RAG als Analyst / Consultant (w|m|d) | findic
zeb consulting · BE, DE
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Responsibilities
- Develop and research graph-based AI approaches, particularly Agentic Graph RAG and agentic systems, for complex knowledge and decision-making processes.
- Design Graph-RAG architectures that integrate structured data, knowledge graphs, and unstructured documents, utilizing hybrid retrieval methods for complex knowledge questions.
- Develop agentic workflows that use graphs for information retrieval, multi-step reasoning, and traceable decision support.
- Investigate the reliability, explainability, data provenance, and controllability of AI systems in highly regulated environments.
- Translate current research into robust, practical data & AI solutions for banks, insurance companies, and other regulated industries.
Requirements
- Possess a very good Master's degree in Computer Science, Mathematics, Data Science, or a related field, and are currently pursuing or about to start a PhD.
- Understand RAG architectures in detail, from chunking and embedding models to hybrid retrieval and reranking, including systematic analysis of retrieval errors.
- Have practical experience implementing RAG or agentic systems, familiar with frameworks like LangGraph or LlamaIndex, or have developed your own orchestration logic.
- Have knowledge of Knowledge Graphs or graph databases and understand the interplay of graph traversal, subgraph retrieval, and vector retrieval.
- Can reproducibly evaluate LLM systems, for example, regarding retrieval Recall@k, ranking quality, correctness of source attribution, verifiability of generated statements, and reliable tool usage.
- Possess very good German language skills (at least C1 level, business fluent) and good English language skills (at least B2 level, fluent) in speaking and writing.
Skills
- Graph-based AI approaches
- Agentic Graph RAG
- Agentic systems
- Graph-RAG architectures
- Hybrid retrieval
- Agentic workflows
- Knowledge Graphs
- Graph databases
- LLM system evaluation
- LangGraph
- LlamaIndex
- German (C1)
- English (B2)
Work Type
- Full-time
- Part-time
- Hybrid
Education Level
- Master's degree
- PhD