Graduate Fellow - AI & Knowledge Mgt.

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
• 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

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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