AI-Powered Resume Screening and Role-Fit Analysis System

Abstract

This study presents an advanced AI-driven Resume Screening and RoleFit System designed to automate and optimize the recruitment process. The proposed framework integrates Large Language Models (LLMs), the OpenAI API, and Machine Learning algorithms within a Python Flask environment to analyze and classify multiple resumes simultaneously. The system performs intelligent parsing, skill extraction, and role matching to evaluate candidate suitability for various job positions. It leverages a custom scoring algorithm to assign quantitative rankings, organizing candidates from top to bottom based on their qualifications, experience, and skill relevance. Additionally, the system categorizes applicants into roles such as frontend, backend, or full-stack according to detected technical proficiencies. By combining automated resume analysis with AI-based decision support, the framework ensures faster, unbiased, and more efficient candidate shortlisting, significantly reducing manual effort and improving hiring accuracy for recruiters and organizations.

Introduction

The recruitment process has become highly competitive, with HR professionals often overwhelmed by large volumes of resumes. Traditional manual screening is slow, labor-intensive, and prone to unconscious biases, leading to inconsistent candidate evaluation. Recent advancements in Artificial Intelligence (AI) and Natural Language Processing (NLP) provide opportunities to automate and improve resume screening.

This project proposes an AI-Powered Resume Screening and Role-Fit Analysis System that extracts and analyzes resume data to match candidates with suitable roles. The system converts resumes (PDF or DOCX) into machine-readable text, extracts key features such as personal details, education, experience, skills, and projects, and stores them in a centralized database. AI and Machine Learning algorithms, integrated with OpenAI’s LLM API, classify candidates based on technical strengths, provide career or skill recommendations, and suggest courses or certifications for profile improvement.

The workflow is fully automated, sequentially processing resumes through uploading, parsing, feature extraction, NLP-based analysis, scoring, and recommendation generation. This approach enhances recruitment efficiency, reduces manual effort, ensures unbiased evaluation, and supports informed decision-making for both recruiters and candidates.

Conclusion

The AI Resume Screening and RoleFit System successfully automates the complex and time-consuming process of resume evaluation and candidate role matching. By integrating LLMs, the OpenAI API, and machine learning algorithms within a Python Flask framework, the system efficiently analyzes large volumes of resumes, extracts relevant skills, and accurately maps candidates to suitable job roles. The inclusion of a scoring and ranking algorithm enables fair and transparent candidate assessment, reducing human bias and ensuring data-driven recruitment decisions. Through its ability to classify applicants into positions such as frontend, backend, or full-stack based on their technical proficiency, the system enhances the precision and speed of the hiring workflow. Overall, this project demonstrates how AI can transform traditional recruitment into a faster, smarter, and more reliable process, supporting HR teams and organizations in making effective and informed talent-selection decisions.

References

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Copyright

Copyright © 2025 Mrs. G. Rajeswari, Atchaya S S, Divya Dharshini T R. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.