My Experiment with AI-Powered Resume Screening

I wanted to know how the application tracking system, or ATS, would evaluate the resume. I've seen our recruitment team members are overflowing with resumes. And scanning them by hand takes hours. Can AI accomplish this more quickly, so I thought of experimenting with natural language processing (NLP) model. Let's #DecodeWithDeepak!

The Problem: Too Much Data, Too Little Time

The Experiment: Let AI Do the Heavy Lifting

I built a Python script using PyMuPDF (fitz) to read PDF resumes page by page and extract important IT skills using regex. I shared library of keywords which are relevant for IT skills. Also provided training data on common words that should be ignored.

The Results were amazing, the smarter & faster screening. No more guesswork. No more wasted time. Running this on my own resumes, I got a keyword-based snapshot of a my expertise. It highlighted following skills :

?? Cloud, Automation, Architecture, Data, Infrastructure, IT Governance, Program, Projects, Products, SQL, C#, Salesforce, Oracle...

Beyond Hiring: Endless Possibilities

The same NLP-driven keyword extraction can be applied to:

?? Academic research – Extract AI and machine learning terms from research papers.

?? Cybersecurity reports – Identify critical threats in security logs.

?? Code reviews – Detect technology stacks in project documentation.

?? The possibilities are endless! AI isn’t replacing humans—it’s making us smarter, more efficient, and future-ready.

#DecodedByDeepak ??Complexity Simplified, Delivering Value! ?? #DecodeWithDeepak

#DeepakOnTech #AI #NLP #Automation #TechHiring #Cloud #Cybersecurity #ResumeScreening #MachineLearning #DataDriven