AI-powered job matching platform that connects candidates with opportunities through intelligent matching algorithms and personalized recommendations.

Intelligent job matching dashboard showing personalized recommendations

Traditional job searching is broken:

• 👤 Candidates: Spam hundreds of applications with low success rates
• 🏢 Employers: Drown in irrelevant resumes, miss qualified candidates
• ⏱️ Time Waste: Average job search takes 3-6 months
• 🎯 Poor Matches: 46% of new hires fail within 18 months due to bad fit

JobLinkAi uses AI to create better matches by analyzing:

• Skills & Experience: Beyond keyword matching
• Culture Fit: Company values alignment
• Career Goals: Long-term aspirations
• Learning Style: Growth trajectory
• Work Preferences: Remote, hybrid, location, company size

Result: Higher quality matches, faster hiring, better retention

• Smart Resume Parsing

  • Automatic skill extraction
  • Experience categorization
  • Achievement quantification

• AI-Powered Matching

  • Personalized job recommendations
  • Match score with explanation
  • Skill gap analysis

• Application Tracking

  • One-click applications
  • Status dashboard
  • Interview scheduler integration

• Career Insights

  • Salary benchmarking
  • Skill demand trends
  • Career path suggestions

• Intelligent Candidate Sourcing

  • AI-ranked candidates
  • Diversity hiring support
  • Passive candidate discovery

• Automated Screening

  • Resume scoring
  • Preliminary assessments
  • Interview scheduling

• Analytics Dashboard

  • Time-to-hire metrics
  • Source effectiveness
  • Diversity insights

• Framework: React 18 with TypeScript
• State Management: Redux Toolkit
• Styling: Tailwind CSS + shadcn/ui
• Forms: React Hook Form + Zod validation
• Charts: Recharts for analytics

• Runtime: Node.js 18+ with TypeScript
• Framework: Express.js
• Database: PostgreSQL (candidate data) + Redis (caching)
• Auth: JWT + OAuth 2.0 (Google, LinkedIn)
• File Storage: AWS S3 (resumes, documents)

• Resume Parsing: spaCy + custom NER models
• Matching Algorithm:
  • Content-based filtering
  • Collaborative filtering
  • Hybrid recommendation system
• NLP: OpenAI API for skill extraction
• Vector Search: Pinecone for semantic matching

• Hosting: AWS (EC2, RDS, S3)
• CI/CD: GitHub Actions
• Monitoring: Datadog
• Email: SendGrid

joblinkai/
├── client/                    # React frontend
│   ├── src/
│   │   ├── components/       # Reusable UI components
│   │   ├── pages/            # Page components
│   │   ├── hooks/            # Custom React hooks
│   │   ├── services/         # API service layer
│   │   ├── store/            # Redux store
│   │   └── types/            # TypeScript types
│   └── package.json
│
├── server/                    # Node.js backend
│   ├── src/
│   │   ├── controllers/      # Request handlers
│   │   ├── models/           # Database models
│   │   ├── routes/           # API routes
│   │   ├── services/         # Business logic
│   │   │   ├── matching.service.ts
│   │   │   ├── resume-parser.service.ts
│   │   │   └── recommendation.service.ts
│   │   ├── middleware/       # Auth, validation, etc.
│   │   └── utils/            # Helper functions
│   └── package.json
│
├── ml/                        # Machine learning models
│   ├── models/               # Trained models
│   ├── notebooks/            # Jupyter notebooks
│   └── scripts/              # Training scripts
│
└── README.md

Node.js 18+
PostgreSQL 14+
Redis 6+
Python 3.9+ (for ML components)

1. Clone the repository

git clone https://github.com/OSP06/JobLinkAi.git
cd JobLinkAi

2. Install dependencies

# Install server dependencies
cd server
npm install

# Install client dependencies
cd ../client
npm install

# Install ML dependencies
cd ../ml
pip install -r requirements.txt

3. Set up environment variables

Create .env file in server directory:

# Database
DATABASE_URL=postgresql://user:password@localhost:5432/joblinkai
REDIS_URL=redis://localhost:6379

# Auth
JWT_SECRET=your-secret-key
OAUTH_GOOGLE_CLIENT_ID=your-google-client-id
OAUTH_GOOGLE_CLIENT_SECRET=your-google-secret
OAUTH_LINKEDIN_CLIENT_ID=your-linkedin-client-id
OAUTH_LINKEDIN_CLIENT_SECRET=your-linkedin-secret

# AI/ML
OPENAI_API_KEY=your-openai-key
PINECONE_API_KEY=your-pinecone-key

# AWS
AWS_ACCESS_KEY_ID=your-access-key
AWS_SECRET_ACCESS_KEY=your-secret-key
AWS_REGION=us-east-1
S3_BUCKET_NAME=joblinkai-resumes

# Email
SENDGRID_API_KEY=your-sendgrid-key

4. Set up database

cd server
npm run migrate
npm run seed

5. Start development servers

Terminal 1 (Backend):

cd server
npm run dev

Terminal 2 (Frontend):

cd client
npm run dev

6. Open in browser

http://localhost:3000

// Extract structured data from resume
const parsedResume = await resumeParser.parse(resumeFile);
// Returns: skills, experience, education, achievements

# Convert skills to vector embeddings
skills_vector = embedding_model.encode(candidate_skills)
job_vector = embedding_model.encode(job_requirements)

const matchScore = calculateMatch({
  skillMatch: 0.40,        // 40% weight
  experienceMatch: 0.25,   // 25% weight
  cultureMatch: 0.15,      // 15% weight
  locationMatch: 0.10,     // 10% weight
  salaryMatch: 0.10        // 10% weight
});

// Rank candidates by match score
const rankedCandidates = candidates
  .map(c => ({ ...c, score: calculateMatch(c, job) }))
  .sort((a, b) => b.score - a.score)
  .slice(0, 20);  // Top 20 matches

• Authentication: JWT tokens with refresh mechanism
• Authorization: Role-based access control (RBAC)
• Data Protection: Encryption at rest and in transit
• Privacy: GDPR compliant, right to deletion
• Rate Limiting: API abuse prevention
• Input Validation: SQL injection & XSS prevention

POST   /api/auth/register
POST   /api/auth/login
POST   /api/auth/refresh
POST   /api/auth/logout
GET    /api/auth/me

GET    /api/candidates/:id
PUT    /api/candidates/:id
POST   /api/candidates/:id/resume
GET    /api/candidates/:id/recommendations
POST   /api/candidates/:id/applications

GET    /api/jobs
GET    /api/jobs/:id
POST   /api/jobs
PUT    /api/jobs/:id
DELETE /api/jobs/:id
GET    /api/jobs/:id/matches

POST   /api/match/calculate
GET    /api/match/recommendations
POST   /api/match/feedback

# Run backend tests
cd server
npm test

# Run frontend tests
cd client
npm test

# Run E2E tests
npm run test:e2e

# Run ML model tests
cd ml
pytest

• Basic job posting & application
• Resume parsing
• Simple matching algorithm
• Candidate dashboard

• Advanced matching algorithm
• Skill gap analysis
• Interview scheduling
• Video interview integration

• Mobile app (React Native)
• Employer analytics
• Referral system
• API for third-party integrations

• ATS integration
• White-label solution
• Advanced reporting
• Multi-language support

• Full-Stack TypeScript: End-to-end type safety
• ML Integration: Combining traditional web dev with AI
• Vector Search: Semantic matching at scale
• User Experience: Balancing automation with control
• Data Privacy: GDPR compliance in AI systems

Contributions welcome! Please check out the Contributing Guide.

This project is licensed under the MIT License - see LICENSE file.

Om Patel

• GitHub: @OSP06
• LinkedIn: om-sanjay-patel
• Portfolio: ompatelportfolio.vercel.app
• Email: [email protected]

• OpenAI for GPT models
• Pinecone for vector database
• LinkedIn API for job data
• Open-source community

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [email protected]

⭐ If you find this project useful, please star the repository!

Built with ❤️ to make job searching better for everyone.