Resume application tracking system using AI

The manual process of screening résumés for job applications is a significant bottleneck in modern recruitment, characterized by being time-consuming, labour-intensive, and susceptible to human bias. This paper presents the architecture of an intelligent Applicant Tracking System (ATS) that leverages Artificial Intelligence, specifically Natural Language Processing (NLP), to automate and enhance the résumé screening process. The proposed system first parses unstructured résumés in various formats to extract key information such as contact details, skills, and work experience using Named Entity Recognition (NER). Subsequently, it employs a deep learning-based semantic analysis model, utilizing pre-trained transformer embeddings (e.g., BERT), to understand the contextual meaning of both the résumé and the job description. A relevance score is then calculated for each candidate using cosine similarity, allowing for an objective and context-aware ranking. This AI-driven approach significantly increases the efficiency of shortlisting candidates, aims to reduce unconscious bias, and provides recruiters with a powerful tool to identify the most suitable applicants from a large pool.