JobIntel-AI

JobIntel-AI is an end-to-end AI-powered platform that matches candidates to the most relevant jobs using advanced NLP, machine learning, and semantic similarity techniques. It also provides skill extraction, recommendations, and an intelligent dashboard for recruiters and job seekers.

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🔍 Features

🔗 Resume-Job Matching

• TF-IDF and semantic vector-based scoring
• Ranks jobs based on content similarity to resume

🧠 Skill Extraction

• Extracts technical and soft skills using:
  • Regex-based keyword matching
  • NLP-based noun phrase extraction (spaCy)
  • Semantic embedding similarity (Sentence Transformers)

📊 Smart Insights

• Categorizes skills by domain (e.g., Programming, DevOps, Web Dev)
• Provides suggestions for missing skills
• Shows frequency and importance of skills in resume

💻 Fullstack Architecture

• Backend: Flask + Python ML ecosystem
• Frontend: Modern React UI with real-time insights
• Database: MongoDB (for job data, resumes, match logs, etc.)
• Modeling: scikit-learn, spaCy, Sentence-Transformers

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🏗️ Architecture

JobIntel-AI
├── backend/                 # Flask backend (resume analysis, skill extraction, job match)
│   ├── main.py
│   ├── job_matcher.py
│   ├── skill_extractor.py
│   └── requirements.txt
├── frontend/                # React (or other) frontend app
│   ├── src/
│   ├── public/
│   └── package.json
├── data/                    # JSON job descriptions or training data
│   └── jobs_descriptions.json
├── .env                     # Environment config
├── README.md                # This file
└── vercel.json              # (Optional) Vercel deployment config

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🚀 Getting Started

1. Clone the Repository

git clone https://github.com/yourusername/jobintel-ai.git
cd jobintel-ai

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🔧 Backend Setup (/backend)

1. Create Virtual Environment

cd backend
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

2. Install Requirements

pip install -r requirements.txt
python -m spacy download en_core_web_sm

3. Run Flask App

python main.py

API will run on: http://localhost:5000

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🖥️ Frontend Setup (/frontend)

1. Install Dependencies

cd frontend
npm install

2. Run Development Server

npm run dev

App will run on: http://localhost:3000

Make sure it fetches backend data from http://localhost:5000 or your deployed API URL.

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📡 API Endpoints

Endpoint	Method	Description
/match	POST	Match resume to job descriptions
/extract-skills	POST	Extract and categorize skills from text
/health	GET	Returns backend health status

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🧪 Sample API Request

/match

{
  "resume_text": "Experienced in Python, Flask, and cloud deployment.",
  "method": "semantic"
}

/extract-skills

{
  "text": "Skilled in Kubernetes, AWS, and Terraform for DevOps automation."
}

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🧠 Skill Domains

• Programming: Python, C++, Java, etc.
• Data Science: Pandas, TensorFlow, etc.
• DevOps & Cloud: AWS, Docker, Jenkins
• Web Development: HTML, CSS, React
• Mobile Development: Flutter, Swift
• Databases: MySQL, MongoDB
• Frameworks: Django, Spring Boot, etc.
• Soft Skills: Communication, Leadership
• Security: Cybersecurity, SIEM
• Project Management: Agile, JIRA, PMP

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⚙️ Deployment

Using Gunicorn for Production

gunicorn main:app --bind 0.0.0.0:5000 --workers 4

Deploy Frontend on Vercel

1. Connect /frontend to your Vercel project
2. Add environment variables for backend API URL
3. Use vercel.json to configure routing

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✅ Future Roadmap

• Resume Parsing from PDF
• Candidate Fit Scoring
• LLM-powered Job Suggestion
• Personalized Learning Resource Links
• Real-time Feedback System