Traditional resume screening is manual, slow, and susceptible to bias, and it struggles to keep pace with today’s application volumes. This paper presents a dual-engine, AI-powered resume screening system designed for transparency and reproducibility. The primary (classical) pipeline encodes resumes and job descriptions using Sentence-BERT (SBERT), computes a resume–job match score via cosine similarity, classifies candidates into 25 job categories using XGBoost, and provides model interpretability through SHAP. In parallel, a prompted large language model (LLM) baseline (GPT-4o/4o-mini) outputs a match score and predicted category for comparative analysis. A Streamlit-based interface integrates both engines to support recruiter workflows and human-in-the-loop oversight. Using a dataset of 962 resumes across 25 categories with an 80/20 stratified split, the system reports standard multi-class performance metrics (classification report and normalized confusion matrix) for the classical pipeline. At the same time, an evaluation harness enables the same artifacts for the LLM baseline on sample subsets. The interface further exposes dataset distribution, top predicted categories, and explanation widgets (SHAP for the classical pipeline; TF-IDF key-term highlighting for the LLM), while applying basic PII filtering. Considerations related to fairness, privacy, and deployment drift are discussed. Comparison with prior SBERT-only systems (e.g., Enhanced Resume Screening using S-BERT) highlights key contributions: (1) a reproducible SBERT→XGBoost→SHAP classification pipeline, (2) an LLM comparator with a standardized evaluation template, and (3) a practitioner-oriented user interface that operationalizes explainability and auditability.

SBERT, XGBoost, SHAP, LLM (GPT-4o, GPT-4o-mini), NLP, Streamlit, Resume Screening, Explainable AI