AI/ML Case Study

Resume Matcher – AI-Powered ATS

The AI-Powered ATS helps bridge the gap between job seekers and recruiters. By using advanced NLP, it doesn't just look for keywords; it understands the context of skills and experience to match candidates with the most relevant job descriptions.

Technology Stack

PythonNLPScikit-learnFlaskspaCy

System Architecture

Parsing

spaCy and PyPDF2 for structured data extraction.

NLP

Word2Vec and Cosine Similarity for semantic matching.

Backend

Flask-based API for handling resume uploads and processing.

The Challenges

Handling various resume formats (PDF, DOCX, Images).

Understanding semantic similarity (e.g., 'React Developer' matching 'Frontend Engineer').

Ensuring fair and unbiased scoring.

The Solutions

Standardized all input formats into a clean text representation using robust OCR and parsing libraries.

Utilized pre-trained word embeddings to capture semantic relationships between different job titles.

Implemented a transparent scoring breakdown that highlights matching skills and missing gaps.

Key Results & Metrics

01

NLP-powered matching

02

ATS compatibility scoring

03

Recruiter workflow optimization