Authors : Vanjula Tejaswini; Dr. Anjana Devi Bondalapati; Dr. Nagesh Vadaparthi

Volume/Issue : Volume 11 - 2026, Issue 10 - October

Google Scholar : https://tinyurl.com/mryfb3vv

DOI : https://doi.org/10.38124/ijisrt/26Oct964

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The increasing use of automated recruitment systems creates a significant challenge for candidates whose resumes may not sufficiently align with target job descriptions. This paper presents PRO-ALIGN, an AI-powered webbased framework for resume–job description matching, Applicant Tracking System (ATS) compatibility analysis, resume optimization, and personalized interview preparation. The system parses resumes and job descriptions, identifies matched and missing skills, and calculates an interpretable compatibility score using weighted components for skills, experience, projects, education, semantic similarity, and formatting. Semantic similarity is supported through vector embeddings and cosine similarity, with offline and term-frequency fallbacks for robust operation. Generative AI capabilities are used for resume rewriting, improvement recommendations, interview-question generation, and answer evaluation. The platform is implemented with React/Vite, Flask, SQLAlchemy, JWT authentication, SQLite/PostgreSQL, and ReportLab. The proposed framework extends conventional resume matching by connecting analysis with actionable resume improvement and interview preparation in a unified workflow.

Keywords : Artificial Intelligence, Natural Language Processing, Resume Matching, Job Description, ATS, Semantic Similarity, Cosine Similarity, Generative AI, Resume Optimization, Interview Preparation.

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