Intelligent Job Application Tracker and Analyzer Using NLP, PySpark and Distributed Cloud Architecture

Abstract

The modern job market demands efficient tools for managing and optimizing job applications. This paper presents the design and implementation of an Intelligent Job Application Tracker and Analyzer, a full-stack distributed system that automates resume parsing, skill gap identification, and job description matching using Natural Language Processing (NLP) and Apache PySpark. The system employs a hybrid cloud architecture built on AWS services including S3, Lambda, EMR, and RDS, orchestrated to deliver scalable, real-time processing. Resumes uploaded by users trigger an automated pipeline that extracts text, performs TF-IDF vectorization, and computes cosine similarity scores against stored job descriptions to produce match percentages and actionable recommendations. The frontend, developed in Next.js, provides an interactive dashboard for tracking application statuses across stages including Applied, Interview, Rejected, and Offer. The backend is powered by Spring Boot with dual database support: PostgreSQL for structured job data and MongoDB for flexible resume JSON storage. Preliminary results from a prototype deployment demonstrate approximately 92% resume text extraction accuracy and 87% skill extraction precision, with end-to-end local pipeline processing completing within 8 to 12 seconds. This system addresses a critical gap in existing job tracking tools by combining intelligent analysis with comprehensive tracking in a single unified platform.