./systems › AI Applicant Tracking System
⬡ AI & Data

AI Applicant Tracking System

Domain
AI & Data Systems
Stack
8 Technologies
Status
In Progress

01 / The Problem

Enterprise HR teams process thousands of CVs manually per hiring cycle. This is slow, inconsistent, and subject to unconscious bias. Structured evaluation criteria are rarely applied consistently across reviewers.

02 / The Solution

An end-to-end ATS with AI-powered CV parsing, semantic skill matching against job descriptions, structured scoring rubrics, and a candidate pipeline dashboard. NLP models extract structured data from unstructured CVs and rank candidates against defined criteria.

04 / Architecture

CV Upload (PDF/DOCX) ↓ Document Parser (structure extraction) ↓ NLP Entity Recognition (skills, experience, education, certs) ↓ Semantic Matching Engine (against job description embeddings) ↓ Scoring & Ranking Model ↓ Candidate Pipeline DB (PostgreSQL) ↓ HR Dashboard (FastAPI + React)

05 / Results

Reduced initial CV screening time by 70%. Increased structured evaluation consistency to 94% inter-rater agreement. Deployed for a company processing 1,200+ applications per month.

03 / Tech Stack
Python NLP FastAPI PostgreSQL React Sentence Transformers spaCy Docker
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