🚀 AI-Powered Talent Match & Job Retrieval System

A high-performance, full-stack recruitment platform that leverages local neural embeddings, semantic vector search, and asynchronous system design to match candidate resumes with job listings based on contextual understanding, not just keywords.

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🧠 🔥 Why This Project Stands Out

Traditional job portals rely on keyword matching, which fails to capture meaning.

👉 This system uses:

• Transformer-based embeddings
• Vector similarity search
• Efficient system-level optimizations

to deliver context-aware job recommendations at scale.

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⚡ Key Features

🧠 Local Inference Engine

• Uses MiniLM-L6-v2 (384-dim embeddings) via Transformers.js
• Runs fully locally using ONNX Runtime
• ❌ No external API calls → ✅ Low latency + zero cost

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🔍 Semantic Vector Search

• Implemented using MongoDB Atlas Vector Search
• Uses HNSW (Hierarchical Navigable Small World) indexing- working
• Enables fast approximate nearest neighbor (ANN) search- working

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⚙️ Decoupled Async Architecture

• Uses node-cron for scheduled background jobs
• Separates:
  • I/O-bound tasks (API fetching)
  • CPU-bound tasks (embedding generation)

👉 Improves scalability and system stability

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🧩 Context-Aware Chunking

• Handles large resumes using:
  • 512-token windowing
  • Recursive text splitting

👉 Works within transformer positional limits while preserving semantic context

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🏗 System Architecture

External APIs (Jooble / Adzuna)
↓
[Ingestion Layer]
(node-cron every 6 hrs)
↓
[Data Cleaning Layer]
(HTML sanitization + regex)
↓
[Embedding Engine]
(MiniLM + ONNX Runtime)
↓
[Vector Database]
(MongoDB Atlas)
↓
[Matching Engine]
(Cosine Similarity)
↓
[REST API Layer]
↓
Frontend

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🔄 Workflow

1. 📥 Data Ingestion

• Jobs fetched every 6 hours from external APIs

2. 🧠 Vectorization

• Background worker:
  • Detects new jobs
  • Generates embeddings (384-dim vectors)

3. 🔍 Semantic Matching

• Resume → embedding
• Compared with job vectors using cosine similarity

4. 📊 Retrieval

• Jobs ranked based on similarity score
• Returned via REST API

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🛠 Tech Stack

💻 Frontend

• React.js
• Redux Toolkit
• Tailwind CSS
• Shadcn/UI

⚙️ Backend

• Node.js
• Express.js

🗄 Database

• MongoDB Atlas (Vector Search)

🧠 Machine Learning

• Transformers.js
• ONNX Runtime
• MiniLM-L6-v2

🔄 Automation

• Node-Cron
• Axios
• Jooble & Adzuna APIs

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📈 Performance & Optimizations

🧠 Memory Management

• Batch processing (20 jobs per cycle)
• Prevents memory overflow during ONNX inference

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⚡ Latency Optimization

• Local embeddings → no API calls
• Vector search → sub-linear retrieval (HNSW)

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🧹 Data Integrity

• HTML sanitization
• Regex-based cleaning

👉 Ensures high-quality embeddings (less noise)

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🚦 Getting Started

✅ Prerequisites

• Node.js v18+
• MongoDB Atlas (Vector Search enabled)

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📦 Installation

git clone https://github.com/nawalkumar/Job-portal-Application.git
cd Job-portal-Application

⚙️ Environment Setup

Create .env file:

  PORT=5001
  MONGO_URI=your_mongodb_uri
  JWT_SECRET=your_secret
  ADZUNA_APP_ID=your_id
  ADZUNA_APP_KEY=your_key
  JOOBLE_KEY=your_key

📥 Install Dependencies

Backend

npm install

Frontend

cd frontend
npm install

▶️ Run Application

Starts backend + cron jobs

npm run dev- both for frontend and Backend

working on:

• Uses HNSW (Hierarchical Navigable Small World) indexing
• Enables fast approximate nearest neighbor (ANN) search

Author:

Naval Kumar,
NIT Sikkim exchange student
@IIT Hyderabad Final Year