LLM Based Resume Screening and Job Matching System using Machine Learning and NLP

This research presents a Machine Learning and Natural Language Processing based system for automated resume screening and job matching. The project focuses on using Large Language Models and semantic embeddings to compare resumes with job descriptions and rank candidates based on relevance. Traditional keyword based hiring systems fail to understand the true meaning of skills and experience. This project solves that problem by using NLP preprocessing TF IDF models and transformer based embeddings to perform accurate resume to job matching. The system improves hiring efficiency reduces bias and helps recruiters identify suitable candidates quickly. It can be applied in real world recruitment platforms applicant tracking systems and job portals.