🚀 Job Finder AI Agent

"The future of job search isn’t more applications. It’s better decisions."

💡 The Philosophy

Most people apply to jobs blindly. Sending resumes into a black hole. Hoping something sticks.

We built something different.

This is Job Finder AI Agent — an autonomous system that turns your LinkedIn profile into a precision job-hunting strategy.

• Not keywords.
• Not mass applications.
• Actual understanding.

This isn't just a job board. It's a career intelligence system that understands you and finds where you belong.

🌟 What It Does

The agent acts like a personal recruiter working 24/7:

• 🕵️ Analyzes your LinkedIn like a recruiter – Digs deep into your professional footprint.
• 🧠 Understands real experience – Ignore the buzzwords; finds the substance.
• 🎯 Stratgeic Matching – Identifies the right roles + domains for your specific growth path.
• 🕸️ Finds Live Jobs – Scrapes YC-backed startups & top tier boards for real opportunities.
• 🔗 Cleans Broken Links – Ensures every opportunity you see is clickable and live.
• 📝 Generates a Career Report – Delivers a clear strategy with match scores and role fit.

All automatically. All in minutes.

🏗️ Architecture & Workflow

Under the hood: Nebius Llama-3.3-70B + Bright Data + Multi-Agent Orchestration + Streamlit.

🤖 The Agents

Our system is composed of 6 Specialized AI Agents, each an expert in its domain:

1. 🕵️ LinkedIn Profile Analyzer

• Role: Extracts deep insights from your professional footprint.
• Intelligence: Distinguishes between actual experience and mere keywords.

2. 🧠 Job Suggestions Engine

• Role: Career path strategist.
• Function: Classifies your profile into high-level domains (e.g., Software Engineering, Product Management) with a confidence score.

3. 🔗 URL Generator

• Role: The bridge between profile and opportunity.
• Function: Dynamically maps your identified domain to specific, high-intent Y Combinator job board URLs.

4. 🕸️ Job Finder (Powered by Bright Data)

• Role: The Hunter.
• Capability: Uses Bright Data's robust infrastructure to scrape real-time job listings without getting blocked.
• Tech: Operates via a Model Context Protocol (MCP) server for secure scraping.

5. 🛠️ URL Parser

• Role: The Cleaner.
• Function: Intelligently extracts unique Job IDs to reconstruct direct, shareable application links.

6. 📝 Summary Agent

• Role: The Career Coach.
• Output: Synthesizes all gathered data into a beautiful Markdown report containing Match Scores, Role Recommendations, and Growth Insights.

📂 Project Structure

bash
job_finder_agent/
├── 📄 app.py              # Main Streamlit Application UI
├── 🧠 job_agents.py       # Agent Logic & Prompt Definitions
├── 🔌 mcp_server.py       # Bright Data Integration (MCP)
├── 📦 requirements.txt    # Component Dependencies
├── 📂 assets/             # Images & Static Resources
└── ⚙️ .env                # Configuration (GitIgnored)

🛠️ Installation & Setup

Prerequisites

• Python 3.10+
• Bright Data Account
• Nebius AI Account

1. Clone the Repository

bash
git clone 

2. Set Up Virtual Environment

bash
# Windows
python -m venv venv
venv\Scripts\activate

# Mac/Linux
python3 -m venv venv
source venv/bin/activate

3. Install Dependencies

bash
pip install -r requirements.txt
# OR
uv sync

4. Configuration

Create a .env file in the root directory:

env
NEBIUS_API_KEY="your_nebius_api_key"
BRIGHT_DATA_API_KEY="your_bright_data_api_key"
BROWSER_AUTH="your_bright_data_browser_auth"

🎮 Usage

1. Launch the App:

   bash
   streamlit run app.py
   

2. Authenticate:

   • Enter your Nebius API Key in the sidebar.

3. Analyze:

   • Paste a target LinkedIn Profile URL.
   • Hit "Analyze Profile".

4. Review:

   • Watch the agents work in real-time.
   • Receive your comprehensive Career Strategy Report within moments.

🤝 Contributing

We welcome contributions to make this agent even smarter!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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