What question did this study set out to answer?

This work aims to enhance recruitment processes by using AI for semantic resume analysis and personalized career recommendations.

June 29, 2026Open Access

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

Key Points

This work aims to enhance recruitment processes by using AI for semantic resume analysis and personalized career recommendations.
Developed an AI platform that employs transformer-based semantic embeddings for resume analysis.
Implemented explainable AI techniques and skill-gap analysis to provide actionable recommendations.
Designed the architecture to be CPU-efficient and deployable in resource-constrained environments.
The platform showed significant improvements in resume-job matching accuracy, enhancing user decision-making.
It provided interpretable score decompositions that increased user trust during the recruitment process.
Demonstrated feasibility in deploying ethical, explainable AI systems in real-world recruitment settings.

Abstract

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.

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Cite This Study

Doyel Mishra (Sat,) studied this question.

synapsesocial.com/papers/6a420b2ff91bb43ea91924c7 https://doi.org/https://doi.org/10.5281/zenodo.20963214

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