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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