Job Autopilot

Abstract

1. Introduction

AI-powered job application automation with GPT-4o, LinkedIn auto-connect, resume optimization, and cold email campaigns. Features AI agents for contact ranking, scam detection, and memory layer for learning from successful outreach.

2. Objective

AI-powered job application automation with GPT-4o, LinkedIn auto-connect, resume optimization, and cold email campaigns. Features AI agents for contact ranking, scam detection, and memory layer for learning from successful outreach.

This project demonstrates how Python, Docker can be applied to a real-world Digital Marketing & SEO problem.

3. Key Features / Modules

• Automated Indeed Scraping via Apify
• AI-Powered Job Scoring (0-10 rating based on your profile)
• Smart Categorization (EdTech, AI PM, Automation, L&D)
• Database Caching (Neon PostgreSQL + Local SQLite fallback)
• Load Cached Jobs (reuse previous searches, save API quota)
• Multi-Format Upload: Support PDF, DOCX, and Markdown master resumes
• Professional Templates: 4 ATS-friendly templates (single/two-column, classic/modern)
• GPT-4o Powered: Resume optimization uses GPT-4o for higher accuracy
• ATS Scoring: Real-time ATS compatibility score with keyword matching
• Job-Tailored Resumes: AI optimizes resume for each job description

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Schlaflied/job-autopilot.git
cd job-autopilot

# 1. Clone the repository
git clone https://github.com/Schlaflied/job-autopilot.git
cd job-autopilot

# 2. Create virtual environment
python -m venv venv
venv\Scripts\activate  # Windows
source venv/bin/activate  # macOS/Linux

# 3. Install dependencies
pip install -r requirements.txt

# 4. Configure environment variables
cp .env.example .env
# Edit .env with your API keys

# 5. Initialize database
python scripts/init_database.py
python scripts/init_coffee_chat_db.py

# 6. Run the application
streamlit run streamlit_app.py --server.port=8502

# 1. Build the image
docker build -t job-autopilot .

# 2. Run the container
# We map port 8502 and allow access to the host's Chrome (CDP)
docker run -p 8502:8502 -p 5000:5000 \
  --add-host=host.docker.internal:host-gateway \
  --env-file .env \
  -v "%cd%/data":/app/data \
  job-autopilot

# Direct script execution
python scripts/linkedin_auto_connect.py --company "google.com" --school "University of Western Ontario" --limit 5

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Export reports to Google Sheets or PDF
• Schedule weekly automated reports
• Add competitor comparison

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. Which marketing or SEO problem does this tool solve?
2. Which data sources or APIs does it use (Search Console, Analytics, social platforms)?
3. Which metrics or KPIs does it report and how are they calculated?
4. How could the output help a business make decisions?
5. How would you schedule it to run automatically?

9. Source Code & License

This project is developed by Schlaflied and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.