An innovative AI-powered job screening system that automates CV-JD matching to revolutionize recruitment. Using NLP and cosine similarity scoring, with the gemma:2b model for summarization and TF-IDF for precise assessment, it offers real-time CV parsing, a user-friendly Streamlit interface, and automated interview scheduling.

AI Enabled JD-CV Matching System

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An system implementing AI to support HR activities

This project aimed to develop an information retrieval system that could identify and rank resumes based on their relevance to a given job description. It utilizes Natural Language Processing (NLP) techniques, specifically Named Entity Recognition (NER) and Vector Space Model (VSM) , to extract relevant features.

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Built an AI-powered ATS with 95%+ accuracy, reducing HR review time by 80%. Enabled bulk processing of 50+ resumes in real time. Developed a chatbot for keyword analysis, accelerating evaluation by 40%.

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A modular AI-powered Job Screening Agent system that automates CV extraction, JD summarization, and candidate-job matching using LLMs and intelligent agents.

The CV-JD Matching System is a Retrieval-Augmented Generation (RAG) framework crafted to enhance the recruitment process by mapping resumes (CVs) to job descriptions (JDs). This system utilizes advanced prompt engineering and scoring algorithms to assess and rank CVs based on the specific requirements outlined in JDs.

Manual recruitment is slow and error-prone, requiring human effort to read job descriptions, evaluate resumes, and schedule interviews. It often misses qualified candidates and lacks an efficient way to assess candidate interest or soft skills. There's a need for an AI-driven system that can automate JD analysis, CV matching, shortlisting