AI-Based Job Skill Analyzer

Authors: Saragadam Tarun, M V Pavan Kumar, Potu Srikanth, Ms R Santhana Lakshmi

Abstract: The rapid advancement of digital recruitment platforms and the exponential growth in job applications have significantly increased the complexity of candidate screening and recruitment management. Traditional recruitment systems largely depend on manual resume screening, which is time-consuming, inefficient, and often unable to accurately identify the most suitable candidates for specific job roles. Furthermore, job seekers frequently struggle to understand industry requirements, optimize their resumes, and identify the skill gaps preventing them from securing suitable opportunities. To address these challenges, this paper proposes an AI-Based Job Skill Analyzer that integrates Natural Language Processing (NLP), Machine Learning, and Web Automation technologies to automate and optimize recruitment workflows. The proposed system extract candidate information from resumes in PDF format, identifies technical and professional skills using NLP algorithms, scrapes real-time job listings from multiple recruitment platforms, and performs intelligent resume-job matching using TF-IDF vectorization and Cosine Similarity algorithms. The system further performs skill gap analysis by comparing candidate competencies with industry requirements and provides personalized recommendations for improvement. A dedicated recruiter dashboard enables recruiters to filter candidates based on skills, match scores, salary expectations, and experience levels, thereby improving recruitment efficiency and decision-making processes. The implementation of this system demonstrates significant improvements in recruitment automation, candidate-job alignment, and hiring accuracy while reducing manual effort and operational costs. The proposed architecture is scalable, secure, and adaptable to future AI-driven recruitment ecosystems.