CareerLens: An AI-Powered Platform for Semantic Resume Analysis and Career Growth Intelligence

CareerLens is an AI-powered platform for semantic resume analysis and career growth intelligence. The system addresses limitations of traditional Applicant Tracking Systems (ATS) by combining transformer-based semantic embeddings, explainable scoring mechanisms, and seniority-aware calibration. The platform integrates resume parsing, semantic similarity computation, skill-gap analysis, and explainable AI techniques to provide transparent and actionable recommendations for candidates. CareerLens employs Sentence-BERT and BGE embeddings for dense semantic matching while incorporating domain-specific weighting strategies to account for experience level and career progression. The proposed architecture is designed to be CPU-efficient, fully open-source, and deployable in resource-constrained environments. In addition to resume-job matching, the system provides personalized skill recommendations, career insights, and interpretable score decomposition to improve user trust and decision-making. This work demonstrates the feasibility of fair, explainable, and semantically informed AI systems for recruitment and career development applications while emphasizing transparency, reproducibility, and ethical AI practices.