AI-Powered Resume Screening System

The AI-Powered Resume Screening System is designed to streamline the recruitment process by using intelligent document analysis to match job candidates with relevant job positions based on their actual skills and experience.

The application acts as an intelligent bridge between a candidate's resume and a database of job opportunities. Users can upload resumes in PDF format, and the system automatically extracts and reads the text content from these files. It scans the resume for a comprehensive list of technical skills, ranging from programming languages like Python, Java, and C++ to specialized domains like Machine Learning, Cloud DevOps, and Web Development.

The system doesn't just count keywords; it understands the relationship between skills through intelligent skill weighting. For example, it recognizes that proficiency in specific libraries like TensorFlow or PyTorch strengthens a candidate's overall score in Machine Learning. By transforming the resume into a mathematical representation (embedding), the AI compares the candidate's profile against a vector database of job descriptions, identifying and ranking the most relevant job positions.

For every potential match, the system provides a 'Similarity Score,' giving recruiters a clear, data-driven metric of how well a candidate fits a specific role. The project utilizes a cutting-edge 'Vector Search' architecture to ensure high accuracy and speed, using advanced language models (specifically the MiniLM transformer model) to understand the context of the text rather than just looking for exact word matches.

The system features a clean, web-based dashboard built with Streamlit that allows recruiters to upload files and view candidate summaries and job matches in real-time. It uses Pinecone, a specialized vector database, to store and retrieve job descriptions as high-dimensional vectors, enabling the system to perform complex similarity searches across thousands of job listings instantly. The system employs sophisticated mathematical techniques using NumPy to calculate scores and handle the data processing required for AI analysis, alongside regex-based analysis to ensure specific technical terms and skill variations are accurately captured.

• Developed an AI-powered resume screening system using vector search and NLP for intelligent candidate matching
• Implemented automated resume digitization with PDF text extraction and parsing
• Built comprehensive skill identification system recognizing programming languages and specialized domains
• Created intelligent skill weighting algorithm understanding relationships between technical skills
• Designed automated job matching system using vector embeddings and similarity scoring
• Integrated Pinecone vector database for high-speed similarity searches across thousands of job listings
• Utilized MiniLM transformer model for context-aware natural language processing
• Developed Streamlit web dashboard for recruiters to upload resumes and view matches in real-time
• Implemented mathematical modeling with NumPy for score calculation and data processing
• Created regex-based analysis system for accurate technical term and skill variation capture
• Designed similarity indexing system providing data-driven metrics for candidate-job fit

Resume Upload Interface

Clean web-based dashboard for recruiters to upload PDF resumes and view candidate summaries

Skill Identification

Automated extraction and identification of technical skills from resumes

Job Matching Results

Ranked job matches with similarity scores showing candidate fit for specific roles