Abstract: The rapid growth of digital recruitment platforms has intensified the need for automated resume screening systems that efficiently evaluate candidate profiles and match them with suitable job roles. However, many existing Applicant Tracking Systems (ATS) operate as opaque, proprietary solutions, limiting transparency and accessibility for job seekers. This paper presents an NLP-driven automated resume analysis and job matching framework designed to democratize access to career optimization tools. The proposed system integrates classical Natural Language Processing (NLP) techniques—including tokenization, lemmatization, Named Entity Recognition (NER), TF-IDF vectorization, and cosine similarity—to evaluate resume quality, simulate ATS compatibility scoring, and recommend relevant job opportunities.

The framework employs a hybrid scoring model combining keyword relevance, section completeness, skill alignment, and formatting compliance to generate interpretable resume scores. Additionally, a similarity-based ranking algorithm matches resumes with structured job descriptions stored in a PostgreSQL database. The system is designed for transparency, determinism, and reproducibility, ensuring explainable outputs suitable for academic and practical deployment. Experimental evaluation demonstrates that the proposed method provides consistent, scalable, and computationally efficient job recommendations while maintaining interpretability—a critical requirement for ethical AI deployment in recruitment technologies.

Keywords: Natural Language Processing (NLP), Applicant Tracking System (ATS), Resume Scoring, Job Recommendation, Skill Gap Analysis, TF-IDF Vectorization, Cosine Similarity, Named Entity Recognition (NER), Machine Learning.

Downloads: | DOI: 10.17148/IARJSET.2026.13327