This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

Opportunity-Shield: AI-Powered Internship and Job Scam Checker

What I Built

Opportunity-Shield is an AI-powered internship and job offer checker designed to help students and first-time job seekers identify potential recruitment scams before accepting an offer.

Many students receive internship offers through emails, messaging apps, and social media, but they may not know how to distinguish genuine opportunities from suspicious ones. Fake recruiters, misleading stipend promises, inconsistent offer details, and suspicious payment requests can put students at risk.

Opportunity-Shield helps users analyze an internship or job offer message and compare two opportunity documents to identify possible warning signs and inconsistencies. It generates a risk assessment, explains suspicious details, suggests questions to ask recruiters, and recommends next steps for verification.

The project is designed for students, fresh graduates, job seekers, and educational communities. In the future, it could also support colleges, career guidance centers, and placement cells that want to help students evaluate opportunities more carefully.

The goal is not to declare every offer genuine or fake, but to help users make better-informed decisions using AI-assisted analysis.

Code

GitHub Repository: https://github.com/komal1101-ngs/Opportunity-Shield

The project is built using Python and FastAPI, with separate modules for evidence preparation and AI-powered analysis. The web interface allows users to submit internship details and review the resulting risk assessment.

How I Built It

I built Opportunity-Shield using Python, FastAPI, HTML, CSS, and Ollama with the Gemma 3 1B model running locally.

The application is organized into these main files:

• app.py — Provides the web interface and FastAPI endpoints.
• model.py — Handles AI-powered analysis using the locally running Ollama model.
• tool.py — Prepares evidence and supporting information for analysis.
• SKILL.md — Documents project instructions and workflow information.

When a user submits an offer, the application processes the provided information, prepares evidence for analysis, and generates a report with potential warning signs, a risk assessment, and suggested verification steps.

One important design decision was to use a local AI model instead of relying on a paid external AI API. This makes experimentation more accessible and lets the AI analysis run on my own computer.

Opportunity-Shield is still a work in progress. Its assessments are intended to support human judgment, not replace independent verification of employers and recruiters.

Why Does Open Innovation Matter?

Open innovation matters because recruitment scams affect students across different communities, colleges, and countries. A tool that helps people recognize suspicious opportunities can become more useful when developers, students, and security researchers contribute ideas, test cases, and improvements.

Using open-source tools such as Python, FastAPI, Ollama, and an open-weight language model made it possible for me to experiment with AI locally without depending on a paid AI API.

Open development also creates opportunities for contributors to improve evidence checks, test the application against different scam patterns, strengthen privacy protections, and make the tool more reliable for real users.

As a beginner exploring AI and open-source development, building Opportunity-Shield has helped me learn how a local AI model can be integrated into a practical application that addresses a real-world problem.

Prize Categories

• Open-Source AI
• Side Hustle