Savannah River National Laboratory is seeking a highly motivated graduate fellow to advance our AI-driven knowledge management capabilities. This fellowship is focused on building next-generation systems for intelligent information retrieval, knowledge graph construction, and multi-agent AI workflows that support complex scientific workflows. The successful candidate will bring graduate-level research experience in large language models, retrieval-augmented generation (RAG), or knowledge representation, and a passion for applying these techniques to real-world challenges across scientific and engineering domains. Design and implement knowledge management pipelines using large language models (LLMs), retrieval-augmented generation (RAG), and vector databases to enable intelligent information retrieval across large, multi-modal document corpora Develop and evaluate multi-agent AI architectures for automated reasoning, summarization, and decision support Build and maintain knowledge graphs and ontologies to represent complex domain relationships and support semantic search Collaborate with cross-functional research teams to integrate AI knowledge tools into existing scientific workflows and applications Author technical documentation, scientific journal articles, and internal reports communicating methods and findings to both technical and non-technical audiences Participate in code reviews and contribute to a shared, well-maintained research codebase Monitor and evaluate emerging developments in LLMs, agentic AI, and knowledge management frameworks, and assess their applicability to ongoing projects Minimum Qualifications Recent graduate (M.S. or Ph.D.) in Computer Science, Data Science, Information Science, or other scientific and engineering disciplines Strong proficiency in Python, including experience with AI/ML libraries such as PyTorch, Hugging Face Transformers, or LangChain Foundational understanding of large language models, prompt engineering, and retrieval-augmented generation (RAG) Experience with or coursework in natural language processing (NLP) or knowledge representation Ability to clearly document and communicate technical research, including writing reports and presenting findings Preferred Qualifications Research experience or publications related to LLMs, knowledge graphs, information retrieval, or multi-agent systems Hands-on experience building end-to-end RAG pipelines or agentic AI workflows Familiarity with knowledge graph construction, ontology design, or semantic web technologies (RDF, SPARQL, OWL) Experience with vector databases or embedding-based search systems Background in a scientific or national security domain (e.g., environmental science, bioengineering, chemistry) is a plus Experience working in a research or government laboratory environment Design and implement knowledge management pipelines using large language models (LLMs), retrieval-augmented generation (RAG), and vector databases to enable intelligent information retrieval across large, multi-modal document corpora Develop and evaluate multi-agent AI architectures for automated reasoning, summarization, and decision support Build and maintain knowledge graphs and ontologies to represent complex domain relationships and support semantic search Collaborate with cross-functional research teams to integrate AI knowledge tools into existing scientific workflows and applications Author technical documentation, scientific journal articles, and internal reports communicating methods and findings to both technical and non-technical audiences Participate in code reviews and contribute to a shared, well-maintained research codebase Monitor and evaluate emerging developments in LLMs, agentic AI, and knowledge management frameworks, and assess their applicability to ongoing projects