An Ensemble-Based End-to-End Framework for Automated Resume Screening Using Machine Learning and Service-Oriented Architecture

Abstract

This study presents an end-to-end framework for automated resume screening leveraging an ensemble of advanced machine learning techniques within a service-oriented architecture (SOA). The framework integrates a diverse set of predictive algorithms designed to evaluate resumes against job descriptions across various domains. The proposed ensemble approach combines individual model predictions through a weighted averaging method to ensure robust accuracy and reliability. The system incorporates efficient back-end architecture for pre-processing, model training, and inference tasks, while a user-centric interface facilitates administrative management and real time resume evaluation. This research addresses key challenges in real-time resume screening by providing a scalable, efficient, and automated solution to enhance the functionality of Applicant Tracking Systems. The findings underscore the transformative potential of machine learning-driven frameworks in optimizing recruitment workflows and advancing human resource management practices.