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PlacementHub – AI-Powered Cloud-Based Placement Management System

A full-stack placement management platform featuring AI-powered resume analysis, real-time chat, and intelligent candidate search, deployed entirely on Microsoft Azure.

Project Modules

  • Student Portal: Enables students to register, authenticate securely, upload their resumes, browse available job postings, and track their application statuses.
  • Company Portal: Allows companies to register, authenticate securely, post new jobs, manage applicants, update application statuses, and search for candidates by skills.
  • AI Resume Processing: An automated pipeline that takes uploaded student resumes and extracts key insights, specifically identifying technical skills.
  • Real-Time Communication: A WebSocket-based chat system enabling instant messaging between candidates and company HR representatives.
  • Candidate Intelligence & Search: An intelligent search system that allows companies to discover candidates based on complex skill queries.

Cloud Services Used

  • Azure App Service
  • Azure SQL Database
  • Azure Blob Storage
  • Azure AI Document Intelligence
  • Azure Functions
  • Azure Web PubSub
  • Azure Cosmos DB
  • Azure Cognitive Search
  • Azure Key Vault
  • Azure Application Insights & Log Analytics

Cloud Services Used and Their Purpose

  1. Azure App Service (Web Apps)

    • Purpose: Hosts the Express.js Node backend API and serves the production-ready React frontend application.

  2. Azure SQL Database

    • Purpose: Acts as the primary relational database to securely store structured data such as user profiles (Students/Companies), Job listings, and Application statuses.

  3. Azure Blob Storage

    • Purpose: Securely manages and stores unstructured binary files, specifically the resume documents (PDFs, DOCX) uploaded by students.

  4. Azure AI Document Intelligence (Form Recognizer)

    • Purpose: Automatically scans, parses, and extracts critical text and technical skills from the uploaded student resumes, replacing manual data entry.

  5. Azure Functions (Serverless)

    • Purpose: Runs background, event-driven processes. Specifically, it executes ResumeProcessor to analyze a resume asynchronously the moment it is uploaded to Blob Storage.

  6. Azure Web PubSub

    • Purpose: Powers the real-time, bi-directional WebSocket chat features, handling persistent connections without overwhelming the backend server.

  7. Azure Cosmos DB

    • Purpose: A globally distributed NoSQL database utilized to rapidly store and retrieve high-velocity, unstructured real-time chat message histories.

  8. Azure Cognitive Search

    • Purpose: Enables advanced, full-text intelligent searching to help companies find candidates based on complex skill matching.

  9. Azure Key Vault

    • Purpose: Centrally manages and securely stores sensitive application secrets like database passwords, API keys, and connection strings.

  10. Azure Application Insights & Log Analytics

    • Purpose: Monitors the live application to detect performance anomalies, log application errors, and analyze user telemetry.

Project Execution Steps

Step 1: Define Requirements and Architecture

  • Store structured user data (Students/Companies) securely in Azure SQL Database.
  • Use Azure App Service to securely host the frontend client and backend API.
  • Incorporate Event-Driven Architecture using Azure Blob Storage and Azure Functions for file processing.

Step 2: Setup Azure Database for Users and Entities

  • Provision an Azure SQL Database instance.
  • Define relational tables with appropriate fields: Students (id, email, passwordHash, skills), Companies, Jobs, and Applications.
  • Secure the databases by restricting network access via Azure Firewall and managing credentials using Azure Key Vault.

Step 3: Implement User Registration and Authentication

  • Develop Express.js routes to handle Student and Company registration.
  • Hash text passwords using bcrypt before storing the user details in the Azure SQL database.
  • Create an authentication route that verifies credentials and returns JSON Web Tokens (JWT) for secure session management.

Step 4: Implement AI Resume Processing

  • Develop a backend endpoint that accepts file uploads and securely transfers the resumes into Azure Blob Storage.
  • Configure a serverless Azure Function using a Blob Trigger to actively watch the storage container.
  • Within the function, utilize Azure AI Document Intelligence to analyze the document, extract candidate skills, and update the student's record in the Azure SQL Database.

Step 5: Implement Job Management and Candidate Discovery

  • Build API controllers allowing Companies to perform CRUD operations on job postings.
  • Incorporate Azure Cognitive Search to allow companies to perform complex keyword searches against the pool of student skills.
  • Develop endpoints for students to apply for jobs, creating junction records in the Applications table.

Step 6: Implement Real-Time Chat Communication

  • Set up Azure Web PubSub to establish and maintain bi-directional WebSocket connections between the client and server.
  • Intercept chat messages and persist them into an Azure Cosmos DB collection for highly available chat history retrieval.

Step 7: Test and Deploy

  • Test all REST API endpoints using Postman and validate real-time WebSocket communication.
  • Deploy the frontend bundle and backend server to their respective Azure App Service instances.
  • Enable and monitor live API usage, throughput, and error rates using Azure Application Insights and monitor backend logs using Azure Log Analytics.