AI Resume Analyzer: A Survey and Implementation Report on Intelligent Resume Evaluation Using Large Language Models

Abstract

AI Resume Analyzer is an intelligent web-based platform designed to automate and enhance the process of resume
evaluation for both job seekers and recruiters. The platform leverages modern artificial intelligence technologies,
particularly Large Language Models (LLMs), to parse, analyze, and score resumes against job descriptions in real
time. Built using a scalable full-stack architecture, the system integrates powerful AI components including natural
language processing, semantic similarity matching, and structured information extraction into a unified resume
analysis pipeline. This architecture allows the system to accurately interpret resume content, identify skill gaps, match
candidate profiles to job requirements, and deliver actionable feedback to users. By combining these technologies with
a modular and responsive interface, AI Resume Analyzer enables flexible deployment, high accuracy, and efficient
performance for individuals and organizations seeking automated talent screening and career guidance solutions.
A key capability of the platform is its intelligent scoring and feedback system, which evaluates resumes across
multiple dimensions including skills match, experience relevance, educational qualifications, and keyword
optimization. The system is optimized to deliver near-instant analysis results, ensuring a smooth and responsive user
experience. The platform integrates seamlessly with modern web technologies and supports PDF and DOCX resume
uploads, enabling users to analyze their documents directly through a browser-based interface without requiring any
installation. The system also supports job description input, allowing recruiters to screen multiple candidates
efficiently.
To simplify the resume improvement process, the platform provides detailed section-wise feedback that guides users
on how to strengthen their resumes. Furthermore, AI Resume Analyzer incorporates a Retrieval-Augmented
Generation (RAG) approach that enables context-aware suggestions by drawing from curated career guidance
knowledge bases. The project report presents the overall system architecture, component design, data flow
mechanisms, database schema, infrastructure technologies, and performance benchmarks, demonstrating that
production-grade AI resume analysis capabilities can be achieved within an open, extensible, and scalable platform
suitable for modern career and recruitment applications.