An intelligent, AI-powered career platform that revolutionizes the job hunting experience through automated resume enhancement, intelligent job matching, AI mock interviews, corporate fellowships, and community-driven networking.
Features • Tech Stack • Getting Started • API Reference • Contributing
The AI Resume Builder & Career Platform is a comprehensive full-stack application designed to streamline and enhance the job search process. By leveraging cutting-edge AI technology (Google Gemini 2.5), real-time communication via Socket.IO, and intelligent automation through BullMQ job queues, this platform provides job seekers with powerful tools to succeed.
We solve the modern job seeker's most painful challenges:
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AI-powered resume enhancement using Google Gemini 2.5 with ATS scoring and Harvard-format templates. |
Smart job alerts with customizable filters (keywords, location, salary, employment type) delivered via real-time sockets. |
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Visual Kanban-style job tracker with status management from Saved all the way to Offered. |
Real-time community platform with channels, posts, direct messaging, and presence indicators. |
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AI-generated improvement suggestions, LinkedIn optimizations, and a drag-and-drop Portfolio Builder. |
Automated job fetching, bulk processing via queues, and one-click resume downloads. |
🤖 AI-Powered Resume Enhancement
- Smart Resume Enhancement: Transform ordinary resumes into ATS-optimized documents
- Professional Summary Generation: AI-crafted summaries tailored to target roles
- Improvement Suggestions: Actionable recommendations to strengthen your resume
- ATS Score Analysis: Get compatibility scores with detailed feedback
- Harvard Template Formatting: Industry-standard resume formatting
🎓 Career Pilot Fellowships
- Corporate Challenges: Companies post real-world challenges for students
- Student Proposals: Students submit proposals with cover letters and pricing
- Escrow Payments: Razorpay integration for secure payments until completion
- Real-time Chat: Direct messaging between corporate and students
🎤 AI Interview Prep
- Mock Interviews: AI-powered interview simulations
- Role-Specific Questions: Tailored questions based on target role
- Real-time Feedback: Instant AI evaluation of responses
- Multi-Round Support: Technical, behavioral, and HR round simulations
🖼️ Portfolio Builder & GitHub Intelligence
- AI Section Enhancement: Enhance your portfolio's hero, projects, and about sections using Gemini AI
- LinkedIn Profile Optimizer: AI-generated headline rewrites and skills gap analysis vs. industry peers
- Theme Selector: Choose from multiple portfolio themes to match your personal brand
- LinkedIn OAuth: Sign in with LinkedIn and auto-import profile data
| Frontend | Backend | Infrastructure |
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Before running the project locally, make sure you have:
- Node.js 18+
- MongoDB instance (local or MongoDB Atlas)
- Redis server
- Firebase project configuration
- Google Gemini API key
git clone https://github.com/anurag3407/career-pilot.git
cd career-pilotcd backend
npm installcd ../frontend
npm installCreate a .env file inside the backend directory and configure:
PORT=5001
MONGODB_URI=your_mongodb_uri
REDIS_HOST=localhost
REDIS_PORT=6379
GEMINI_API_KEY=your_api_key
RAZORPAY_KEY_ID=your_key
RAZORPAY_KEY_SECRET=your_secretYou may also need a .env file inside the frontend directory depending on your local setup.
Open two terminals.
cd backend
npm run devBackend runs on:
http://localhost:5001
cd frontend npm run dev
After starting both servers:
- Frontend runs on: `http://localhost:5173`
- Backend runs on: `http://localhost:5001`
---
### Verify Installation
After starting both frontend and backend servers:
- Open `http://localhost:5173`
- Verify the frontend loads successfully
- Check backend logs for database/API connection errors
- Ensure Firebase and MongoDB connections initialize correctly
---
## 📜 Available Scripts
### Frontend
```bash
npm run dev
npm run build
npm run lint
npm run dev
npm startView Directory Structure
career-pilot/
├── backend/
│ ├── src/
│ │ ├── config/ # Firebase, AI, Socket, DB setup
│ │ ├── controllers/ # Route logic
│ │ ├── middleware/ # Auth, Rate limiting, Uploads
│ │ ├── models/ # Mongoose Schemas (User, Job, Resume)
│ │ ├── routes/ # Express Routers
│ │ └── services/ # BullMQ Queues, Emails, Integrations
├── frontend/
│ ├── src/
│ │ ├── components/ # UI, Community, Portfolio
│ │ ├── context/ # Auth & Socket contexts
│ │ ├── hooks/ # Custom React hooks
│ │ ├── pages/ # Route views
│ │ └── services/ # API and Socket instances
└── firebase/ # Security Rules & Indexes
New contributors should read ARCHITECTURE.md early, as it includes high-level system diagrams, data-flow charts, and security notes.
View API Routes & Examples
Most endpoints require a Firebase ID Token passed as an Authorization header.
# Verify Token
GET /api/auth/verify# Upload PDF
POST /api/upload
# Enhance Resume via Gemini AI
POST /api/enhance
Content-Type: application/json
{
"resumeText": "...",
"jobRole": "Frontend Engineer"
}
# ATS Analysis
POST /api/enhance/ats-analysis# Create Job Alert
POST /api/job-alerts
# Track Application Pipeline
POST /api/job-trackerFor complete documentation, see the API Reference Guide.
View Solutions to Common Issues
- Verify your
MONGODB_URIin backend.env - Ensure MongoDB service is running
- Check IP whitelist if using MongoDB Atlas
- Ensure Redis server is installed and running
- Verify
REDIS_HOSTandREDIS_PORT
- Change the
PORTvalue in.env - Stop other Node instances:
killall node(Mac/Linux)
We welcome contributions! Please see our CONTRIBUTION.md for the Development Setup, Pull Request Process, and Coding Standards.
Thank you to all the amazing contributors who have helped improve this project.
Your efforts, pull requests, ideas, and support make this project better every day and help grow an incredible open-source community 🚀
This project is licensed under the MIT License - see the LICENSE file for details.