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Job Description: Research : - On the relative strength and weaknesses of GraphRAG in the use case of handling large quantity of unstructured documents of various language standards
Solution Evaluation: - Using Naïve RAG as baseline to benchmark again GraphRAG in terms of accuracy, response latency and token cost
- Research on best performance embedding technology for multi-thousands document set
Requirement: - Currently enrolled in Msc level Machine learning/Data Science program - Familiarity with GraphDB and NLP concepts - Strong analytical skills and attention to detail
Preferred Skills: - Engineering level of understanding of CNN and RNN neural network skills
- Proficiency in Python and JavaScript development language
- Proficiency in written English and Chinese language skill
- Hands on working knowledge of Navie RAG workflow; including rewrite, re-rank, embedding, inferencing, chain-of-though prompting skill
- Hands on working knowledge of knowledge graph DB like Neo4j
- Basic understanding of operation principle of Docker technology
- Hands on working knowledge of mainstream foundation LLM, Reasoning and Multi-modal models
- Hands on working knowledge of mainstream OCR models
- Ability to work independently with minimal supervision
Job Category: Temporary Employee
Posting End Date: 30/03/2026