Final Year Project

Project Title

HireHub

Description

The resume screening project utilizing LlamaIndex aimed to automate and enhance the hiring process by leveraging natural language processing (NLP) and semantic search to match job descriptions with relevant resumes efficiently. The system indexed resumes into a vector space using embeddings, enabling fast and accurate querying based on semantic similarity rather than just keywords. This approach significantly reduced manual effort, improved matching accuracy, and minimized bias, making the screening process more scalable and cost-effective. Key benefits included faster resume processing, better candidate-job alignment, and reduced recruiter workload, though challenges like data quality and bias in training data needed addressing. Future work could focus on integrating with Applicant Tracking Systems (ATS), adding multilingual support, and refining NLP models for even greater accuracy and fairness. Overall, the project demonstrated the potential of AI-driven tools to revolutionize recruitment workflows.