In the modern job market, hiring managers and talent acquisition teams face an overwhelming influx of job applications. For a single opening, hundreds of resumes are submitted, each with unique formatting, fonts, layouts, and styles. Manually reading through each file is a huge bottleneck that costs teams countless hours.
To solve this, I built the AI Resume Analyzer—a lightweight, cloud-native application that leverages Natural Language Processing (NLP) and Machine Learning (ML) to automatically parse PDF resumes, categorize candidates into primary professional domains (e.g., DevOps, Frontend, Data Science), analyze their skills, and suggest missing competencies to fill the gap.
In this blog, I will walk you through the architecture, the machine learning pipeline, NLP extraction, and how I deployed it for free on the cloud.
Instant PDF Extraction: Extracts and cleans raw text from unstructured PDF formats in under 300ms.
AI-Driven Domain Classification: Classifies resumes into matching job roles (like Data Science or Design) with confidence percentages using an ML model.
