JobGuard AI：以本地 AI 和開放權重模型偵測招聘騙案

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

What I Built

JobGuard AI — Local AI-Powered Recruitment Scam Detector 🛡️

Job hunting is already challenging, and recruitment scams make it even harder. Fake job offers, phishing messages, requests for upfront payments, and fraudulent interview processes can put job seekers at risk.

I built JobGuard AI, a web application that analyzes recruitment messages and job offers to identify potential scam indicators and help people make more informed decisions before trusting an offer.

What it does:

• Analyzes job advertisements, recruitment messages, and suspicious offers.
• Uses a rule-based engine to identify warning signs.
• Uses a locally running AI model to help analyze the message and explain potential risks.
• Presents risk assessments and indicators through a web dashboard.
• Is designed to run AI inference locally rather than relying on a paid external AI API.

The intended users include job seekers, students, fresh graduates, and career communities. Placement cells and recruitment-support communities could also benefit from this type of tool.

The project is currently a prototype, and its accuracy and potential business model need further real-world validation.

Demo

🎥 Watch the JobGuard AI demo video:

▶ Watch the Demo on Google Drive

The video demonstrates the application running locally.

Code

The repository contains the frontend, backend, application configuration, documentation, and tests.

Contributions, suggestions, bug reports, and improvements are welcome!

How I Built It

I built JobGuard AI using a full-stack architecture combining web development, local AI inference, and deterministic rule-based analysis.

Technology stack:

• Frontend: React and Vite
• Backend: Java and Spring Boot
• AI inference: Ollama running locally
• Open-weight model: Qwen3 4B
• Testing: JUnit-based backend tests and API testing with Postman
• Containerization: Docker and Docker Compose
• Database support: MySQL-related configuration and persistence options, with persistence disabled in the current local setup

The application combines a rule-based safety layer with local AI analysis. The rules help identify known warning signs, while the AI model provides additional contextual analysis.

Using Ollama allows the application to run inference on my own machine without depending on a paid external AI inference API. This gives me more control over costs, data handling, and experimentation.

Why Does Open Innovation Matter?

Open innovation made it possible for me to build an AI-powered application without depending entirely on proprietary AI services.

Open-weight models and tools such as Ollama let developers experiment with local inference, inspect their application architecture, and build around models that can run on their own hardware.

For a project that analyzes potentially sensitive recruitment messages, local processing can also reduce the need to send message contents to an external AI provider, depending on how the application is configured and used.

Open-source frameworks such as React and Spring Boot made it possible to connect the user interface, backend services, and AI inference into one application.

Most importantly, open innovation gives individual developers and students the opportunity to experiment, learn, build, and share useful tools with the community.