LinkedIn Profile Search and Analysis System

An enterprise-grade automated recruitment solution that leverages AI technology to streamline candidate sourcing and evaluation through LinkedIn profile analysis.

Project Overview

Introduction

The LinkedIn Profile Search and Analysis System is a comprehensive recruitment automation platform that combines advanced LinkedIn profile discovery with Claude AI-powered candidate evaluation. This system helps recruiters, hiring managers, and HR administrators significantly reduce manual screening effort while improving candidate match quality.

Key Features

• 🔍 Automated LinkedIn profile discovery and extraction
• 🤖 AI-powered candidate evaluation using Claude
• ⚡ Real-time search and analysis capabilities
• 🔒 Secure profile data management
• 📊 Advanced analytics and reporting
• 🔄 Enterprise system integration
• 📋 Compliance and security features
• 🏗️ Scalable microservices architecture

System Architecture

Architecture Overview

The system implements a modern microservices architecture designed for scalability, reliability, and maintainability:

graph TD
    A[Web Frontend] --> B[API Gateway]
    B --> C[Search Service]
    B --> D[Analysis Service]
    B --> E[Data Service]
    C --> F[LinkedIn API]
    D --> G[Claude AI]
    E --> H[(Databases)]

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Core Components

• Frontend: React 18+ with Material-UI 5

  • Modern component-based architecture
  • Responsive design
  • Real-time updates

• API Gateway: Node.js 18 with Express

  • Request routing and validation
  • Authentication and authorization
  • Rate limiting and security

• Services:

  • Search Service (Python 3.11)
  • Analysis Service (Python 3.11)
  • Data Service (Java 17 Spring Boot)

• Data Layer:

  • PostgreSQL 15+ (structured data)
  • MongoDB 6.0+ (profile data)
  • Redis 7.0+ (caching)
  • Elasticsearch 8.9+ (search)

Quick Start

# Clone the repository
git clone https://github.com/organization/linkedin-profile-search.git

# Install dependencies
npm install    # Frontend
pip install -r requirements.txt  # Python services
./mvnw install  # Java services

# Configure environment
cp .env.example .env
# Edit .env with your configuration

# Start development environment
docker-compose up -d

Development

# Start frontend development server
cd web/
npm run dev

# Start backend services
cd backend/
docker-compose up -d

# Run tests
npm test        # Frontend
pytest          # Python services
./mvnw test     # Java services

Deployment

# Build production images
docker-compose -f docker-compose.prod.yml build

# Deploy to production
kubectl apply -f k8s/

# Monitor deployment
kubectl get pods -n production

Security and Compliance

Security Features

• OAuth 2.0 authentication with MFA
• AES-256 encryption for data at rest
• Role-based access control (RBAC)
• Web Application Firewall (WAF)
• DDoS protection
• Comprehensive audit logging

Compliance

• GDPR compliance for data privacy
• SOC 2 Type II certified
• ISO 27001 certified
• CCPA compliance
• LinkedIn Terms of Service compliant

Documentation

• Contributing Guidelines
• Backend Services Documentation
• Frontend Application Documentation
• License Information

Support

For technical support or questions:

• 📧 Email: [email protected]
• 💬 Slack: #linkedin-search-support
• 📝 JIRA: Project Board

License

This project is proprietary software. See the LICENSE file for details.

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