End-to-end talent recruitment framework for Talent Corporation Malaysia Berhad

This project demonstrates how deep learning can modernize talent recruitment through an end-to-end framework centered on the Bidirectional Encoder Representations from Transformers model. The resulting Candidates Evaluation List for Employment Selection and Talent Acquisition Ranking system analyzes resumes against job descriptions to extract semantic context, significantly improving matching precision and helping human resource professionals identify top candidates. By featuring interactive data visualization tools, the system streamlines screening, lowers operational costs, accelerates hiring decisions, and removes manual bias to foster a fairer selection process. Ultimately, this approach supports national digital workforce goals like the New Industrial Master Plan 2030 and offers a scalable hiring solution for organizations nationwide.