How to Build an AI Resume Screening Project

You want to build an AI resume screening project, and the honest answer is that you can do it with free tools, a public dataset, and a few weekends of work. The goal is a system that takes a job description and a pile of resumes, then returns a ranked list with scores and reasons. You do not need a big budget or a team; you need a clear scope, clean data, a simple model, and a way to show it off.

Plan the Scope Before You Write Code

Decide what your system will actually do. The most useful version parses resumes (PDF or Word), extracts skills, experience, and education, then scores each resume against a job description. Keep it to three to five features for a first version. Over-scoping is the fastest way to abandon the project.

Write down the inputs and outputs. For example: input is a job description and a folder of resumes; output is a ranked list with a score from 0 to 100 and a short explanation like "Matched 8 of 10 required skills." That explanation matters, because it makes your project credible and shows you thought about fairness.

Collect Data and Parse Resumes

You need resumes to test on. Public datasets exist on sites like Kaggle, or you can generate your own synthetic resumes with a Python library like Faker. Aim for a few hundred samples across different industries and career levels. Anonymize everything: remove names, emails, and phone numbers before you use the data.

For parsing, use a Python library like PyPDF2 or pdfplumber to extract text from PDFs. Then use spaCy for natural language processing: it can recognize entities like job titles and skills. Handle variations like "Sr. Software Engineer" versus "Senior Developer" with fuzzy matching. The parsing step is where most projects fail, so test it on messy resumes early.

Label a small portion of your data manually. Mark each resume as a match or not for a specific job description. This gives you a way to measure accuracy later.

Build the Scoring Model

Start simple. Convert resume text and the job description into vectors using TF-IDF, then compute cosine similarity. A score above a threshold like 0.7 means a strong match. This baseline will work surprisingly well and is easy to explain in an interview.

Then improve it. Fine-tune a pre-trained model like BERT from Hugging Face to understand context, so it can tell the difference between "Java developer" and "coffee enthusiast." Train a classifier with scikit-learn to predict match versus no match. Combine the model output with a weighted scoring function: skills match, experience alignment, education, and maybe soft skills inferred from the text.

Test on data you did not train on. Measure precision (how many of the top-ranked resumes are actually good) and recall (how many good resumes you did not miss). If your system only works on your training data, it is overfitting, and that is the most common mistake in projects like this.

Add a Simple Interface and Deploy

Build a basic web app with Streamlit or Flask so someone can upload a job description and resumes, then see the ranked results. Add a visualization, like a bar chart of scores, using a library like Plotly. This turns your code into a demo people can actually try.

Deploy it so it is accessible. A free tier on a cloud platform works for a portfolio project. Put the code on GitHub with a README that explains the architecture, the data, the model, and the limitations. Include a short demo video. Write down what your system does well and where it fails, like handling non-English resumes or unusual formats. That honesty makes a better impression than a perfect demo.

Once it is live, use it on real job descriptions and your own resume. If your system ranks you low for a job you are qualified for, that is a useful bug to fix. It also gives you a story to tell: you built a tool, tested it, and improved it based on what you found.

When the project is done, use it as your portfolio centerpiece. If you want help polishing your own resume and LinkedIn to match the roles you are targeting, Destava's AI career manager can build and fix your resume, improve your profile, and draft posts that feature your work.