AI Resume and Job Matching System using NLP

An intelligent web application that automatically matches candidates with suitable job opportunities using Natural Language Processing and semantic similarity algorithms. Features real-time job discovery, a Python NLP microservice, and a professional SaaS-style dashboard.

Features

• 📄 Resume Upload & Parsing — Upload PDF/DOCX resumes; AI extracts skills, experience, education, and certifications
• 🧠 NLP Skill Extraction — Tokenisation, stop-word removal, named entity recognition (spaCy), and keyword extraction
• 🎯 Semantic Job Matching — Cosine similarity scoring with sentence-transformers for deep semantic understanding
• 🌐 Real-Time Job Discovery — When no DB jobs match, automatically scrapes RemoteOK and other sources
• 📊 Skill Gap Analysis — Visual breakdown of matched vs missing skills with learning suggestions
• 💼 Job Recommendations — Ranked job listings based on resume match score
• 🏢 Recruiter Dashboard — Post jobs, browse candidates, compare match scores
• 👤 Candidate Dashboard — Resume insights, top matches, skill cloud visualization
• 🔐 JWT Authentication — Secure login/register with rate limiting and input validation
• 🐳 Docker Support — Full stack deployable with docker-compose

Tech Stack

Layer	Technology
Frontend	React.js 19, Tailwind CSS v3, React Router v6, Axios
Backend	Node.js, Express.js
Database	MongoDB (Mongoose)
NLP / AI	Python microservice: spaCy, sentence-transformers (all-MiniLM-L6-v2), Flask
Matching	Node.js: Skill overlap + Jaccard similarity; Python: Cosine similarity on embeddings
Auth	JWT (jsonwebtoken), bcryptjs
File Parsing	pdf-parse, mammoth (DOCX)
Job Discovery	RemoteOK public API, fallback template generation
Deployment	Docker, docker-compose, Vercel (frontend), Render/Railway (backend)

Project Structure

├── backend/
│   ├── config/              # MongoDB connection
│   ├── controllers/         # Business logic (auth, resume, job, match, scrape)
│   ├── middleware/           # Auth, file upload, rate limiter
│   ├── models/              # Mongoose schemas (User, Resume, Job, Match, ScrapedJob)
│   ├── routes/              # Express API routes
│   ├── tests/               # Jest unit tests (28 tests)
│   ├── utils/               # NLP processor, semantic matcher, job scraper
│   └── server.js
├── frontend/
│   ├── src/
│   │   ├── components/      # Navbar (with mobile menu), PrivateRoute
│   │   ├── context/         # AuthContext (JWT state)
│   │   ├── pages/           # Home, Dashboard, ResumeUpload, JobRecommendations,
│   │   │                    # SkillAnalysis, SavedJobs, JobList, PostJob, ...
│   │   ├── api.js           # Axios instance with auth interceptor
│   │   └── App.js           # Router & layout
│   ├── tailwind.config.js
│   └── package.json
├── nlp-service/             # Python NLP microservice
│   ├── app.py               # Flask app: /parse-resume, /match, /extract-skills
│   ├── requirements.txt
│   ├── Dockerfile
│   └── README.md
├── docker-compose.yml       # Full stack deployment
└── README.md

Getting Started

Option A — Docker Compose (Recommended)

git clone <repo>
cd AI-Resume-and-Job-Matching-System-using-NLP

# Start all services
docker-compose up --build

Services start at:

• Frontend: http://localhost:3000
• Backend API: http://localhost:5000
• NLP Service: http://localhost:8000
• MongoDB: localhost:27017

Option B — Manual Setup

Prerequisites

• Node.js 18+
• MongoDB (local or Atlas)
• Python 3.11+ (for NLP service, optional)

Backend

cd backend
cp .env.example .env      # edit MONGO_URI and JWT_SECRET
npm install
npm start                 # production
npm run dev               # development (nodemon)
npm test                  # run 28 unit tests

Python NLP Service (optional)

cd nlp-service
pip install -r requirements.txt
python -m spacy download en_core_web_sm
python app.py             # starts on port 8000

Frontend

cd frontend
npm install
npm start                 # development server (port 3000)
npm run build             # production build

API Endpoints

Authentication

Method	Path	Description
POST	/api/auth/register	Register user
POST	/api/auth/login	Login — returns JWT token
GET	/api/auth/profile	Get current user profile

Resume

Method	Path	Description
POST	/api/resume/upload	Upload and parse PDF/DOCX resume
GET	/api/resume/me	Get own parsed resume

Jobs

Method	Path	Description
GET	/api/jobs	List all active jobs
POST	/api/jobs	Create job posting (recruiter)
POST	/api/jobs/match	Compute match scores vs all jobs
GET	/api/jobs/recommendations	Ranked recommendations (auto-discovers if empty)
POST	/api/jobs/scrape	Trigger real-time job discovery from web
GET	/api/jobs/scraped	List all discovered jobs (with search/filter)

Candidate

Method	Path	Description
GET	/api/candidate/profile	Dashboard data
GET	/api/candidate/skill-gap/:jobId	Skill gap analysis for a specific job
GET	/api/candidate/recruiter/candidates	All candidates (recruiter only)

Job Discovery Pipeline

Resume Upload
     ↓
NLP Parsing (skills, experience, education)
     ↓
Search DB Jobs (MongoDB)
     ↓
IF jobs exist → semantic matching → return ranked results
     ↓
IF no jobs → trigger web scraping (RemoteOK API)
     ↓
Store scraped jobs in MongoDB (ScrapedJob collection)
     ↓
Run semantic matching
     ↓
Return ranked results

NLP Architecture

Node.js NLP Processor

1. Tokenisation — Lowercase, strip punctuation, split into tokens
2. Stop-word removal — Filter 80+ common English stop words
3. Skill extraction — Match against 150+ technical skills (multi-word aware)
4. Section parsing — Heuristic detection for Experience, Education, Certifications, Projects
5. Semantic matching — Skill overlap (80%) + Jaccard token similarity (20%)
6. Alias normalisation — "nodejs" ≡ "node.js" ≡ "node", "reactjs" ≡ "react"

Python NLP Service

1. spaCy NER — Named entity recognition (ORG, GPE, DATE, PERSON)
2. Sentence Transformers — all-MiniLM-L6-v2 generates dense 384-dim embeddings
3. Semantic scoring — Cosine similarity: 60% semantic + 40% skill overlap
4. Graceful degradation — Works without heavy dependencies, with reduced quality

Environment Variables

Backend (.env)

PORT=5000
MONGO_URI=mongodb://localhost:27017/ai_resume_matching
JWT_SECRET=your_long_random_secret_here
REDIS_URL=redis://localhost:6379
NLP_SERVICE_URL=http://localhost:8000

NLP Service (.env)

PORT=8000
DEBUG=false