About the Role
You will help design domain knowledge graph ontologies and schemas, including entities, relationships, attributes, and events. You will build LLM-agent knowledge extraction pipelines, create agent workflows, perform knowledge fusion and disambiguation, write results to graph databases, and experiment with GraphRAG and KGQA systems.
Requirements
•Be a current undergraduate or higher student in computer science, artificial intelligence, NLP, data science, or a related field
•Use Python and demonstrate independent debugging ability
•Understand knowledge graph concepts including entities, relationships, triples, ontologies, RDF, OWL, SPARQL, and Cypher
•Understand agent paradigms including planning, tool use, memory, reflection, and multi-agent systems
•Have used at least one agent or LLM framework
•Understand prompt engineering and large language model APIs
•Understand NLP tasks including named entity recognition, relation extraction, event extraction, text classification, and vector retrieval
Responsibilities
•Design domain knowledge graph ontologies and schemas
•Build LLM-agent knowledge extraction pipelines
•Design agent workflows for planning, tool use, memory, reflection, and result validation
•Perform entity alignment, conflict detection, and quality evaluation
•Write knowledge graph results to graph databases
•Apply GraphRAG and KGQA for retrieval, reasoning, and multi-hop question answering
•Track relevant advances, reproduce experiments, and share technical findings internally
