Intelligent Resume Matching System

Optimally efficient and smart resume screening is a key factor towards optimizing the recruitment processes, particularly with the recent huge number of applicant resumes in modern day labor markets. The contribution put forward herein suggests an Intelligent Resume Matching System to efficiently automate shortlisting resumes against vacancy announcements based on powerful natural language processing (NLP) methodologies and machine learning approaches The system makes use of BERT-based sentence transformers to create high-dimensional contextual embeddings from job postings and resumes both, directly derived from PDF documents through pdfplumber. To measure candidate-job fit numerically, the system calculates cosine similarity between embeddings and enables dynamic threshold-based filtering. Candidates whose resumes are at or above similarity threshold are shortlisted and informed through automated emails. Additionally, the system has a feedback mechanism powered by a large language model (LLM) to suggest personalized messages for unselected candidates, providing constructive feedback on possible skills or experience gaps. The proposed system not only increases the accuracy of resume-job matching but also minimizes manual labor and guarantees customized candidate interaction. Through the integration of NLP-based embedding methods with automation and interpretable feedback, the system proves to have potential for large-scale deployment in contemporary hiring pipelines.