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This paper proposes an AI-powered resume screening and job description matching system that automates candidate evaluation using NLP and machine-learning text similarity. Candidate PDFs are parsed and analyzed to extract skills, qualifications, certifications, and experience, then compared with job requirements via keyword overlap and TF-IDF cosine similarity. 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Human recruiters spend a disproportionate amount of time manually reviewing resumes, leading to delays, inconsistencies, and potential bias in candidate evaluation. This paper presents the design, development, and evaluation of an AIpowered Resume Screening and Job Description Matching System that leverages Natural Language Processing (NLP) and machine learning-based text similarity techniques to automate candidate evaluation. The proposed system accepts candidate resumes in PDF format, extracts technical skills, qualifications, certifications, and experience using an NLP pipeline built on NLTK and spaCy, and compares the extracted information against job description requirements through keyword matching and TF-IDF cosine similarity scoring via Scikitlearn. The backend is developed using Python and Flask, while the frontend is constructed with HTML5, CSS3, and JavaScript, delivering a responsive and recruiter-friendly web interface. The system generates a quantitative skill match percentage alongside detailed insights into matched and missing skills, enabling data-driven hiring decisions. Experimental testing across multiple job profiles and resume formats demonstrated reliable skill extraction accuracy and match percentage consistency. The modular system architecture supports future enhancements including transformer-based semantic matching, multi-resume batch processing, AI-based candidate ranking, and cloud deployment. This work contributes a practical, scalable, and deployable solution to the growing field of intelligent recruitment and talent acquisition automation.  \nIndex Terms—Resume Screening, Job Description Matching, Natural Language Processing, Skill Extraction, Recruitment Automation, Flask, Python, TF-IDF, Cosine Similarity, Text Mining  \nI. INTRODUCTION  \nThe global job market has grown increasingly competitive, with organizations receiving hundreds or thousands of resumes for each advertised position. According to industry estimates, a recruiter spends an average of six to seven seconds scanning a single resume during initial screening, making it virtually impossible to conduct a thorough and consistent evaluation at scale. This manual process is not only timeintensive but also susceptible to human bias, fatigue-induced errors, and inconsistency in evaluation criteria across different recruiters within the same organization.  \nThe emergence of Artificial Intelligence (AI) and Natural Language Processing (NLP) has opened transformative possibilities for automating and improving the recruitment workflow. Intelligent systems capable of reading, understanding, and evaluating unstructured resume documents against structured job requirements offer recruiters a powerful tool to reduce workload, improve consistency, and make more informed shortlisting decisions. These technologies represent a convergence of information retrieval, text analytics, and machine learning that is increasingly being adopted across enterprise hiring platforms.  \nThis paper presents the AI Resume Screening and Job Description Matching System, a web-based application developed using Python, Flask, and NLP libraries. The system accepts candidate resumes uploaded in PDF format and processes them through a multi-stage NLP pipeline to extract relevant skills, qualifications, and experience. The extracted candidate profile is t","cbCait6Pa8bQUENg","https://ap.wps.com/l/cbCait6Pa8bQUENg","pdf",373547,"English","# Abstract\n# Introduction\n# Related Work\n# System Methodology\n## NLP Pipeline\n## Matching Algorithm\n# Implementation\n## Architecture\n## User Interface\n# Experimental Results and Discussion\n# Limitations\n# Future Work\n# Conclusion","[{\"question\":\"How does the system compare a candidate resume with a job description?\",\"answer\":\"It extracts skills, qualifications, certifications, and experience from resume PDFs, then measures overlap with job requirements using keyword matching and TF-IDF cosine similarity scoring.\"},{\"question\":\"What is the output provided to recruiters after matching?\",\"answer\":\"The system generates a quantitative skill match percentage and a detailed skill gap analysis showing matched and missing skills.\"},{\"question\":\"What technologies are used to build the system?\",\"answer\":\"The NLP pipeline uses libraries such as NLTK and spaCy, similarity scoring relies on scikit-learn, and the backend is implemented with Python and Flask with a responsive HTML5/CSS3/JavaScript frontend.\"}]","AI Resume Screening and Job Description Matching System - paper | PDF",1789321299]