Paper Title
Smart Hire X: Implementation of an Explainable NLP and Semantic Embedding–Based Resume Parsing and Job Matching Framework

Abstract
The fast emergence of online recruitment sites has resulted in an exponential growth of the number of applicants per available position. Manual review is inefficient and prone to bias in such circumstances. The conventional resume filtering technique that operates based on keywords ignores the meaning and significance of resumes compared to job descriptions. This paper highlights the development of the application of SmartHireX – an explainable NLP-driven resume parsing and job matching framework based on SBERT embedding vectors and cosine similarity measure. The framework adopts the MERN stack architecture that includes React.js for the front end interface, Node.js and Express.js for the server, MongoDB Atlas for structured data storage, and SBERT model for deep text data semantics embedding. SmartHireX executes within a secure environment along with the implementation of cloud-based database, which ensures higher data confidentiality and performance. The experimental analysis proves the superiority of the proposed solution in comparison with conventional resume parsing approaches in terms of semantic matching. Keywords - Resume Parsing, Sentence-BERT, Semantic Embeddings, Job Matching, NLP, Explainable AI, MERN Stack, Candidate Ranking