Virtual Expo 2025

JobSphere

Envision CompSoc

Gmeet Link: https://meet.google.com/bvk-krta-ugu

Aim

To create a high-performance, user-friendly platform for job seekers that enables intelligent filtering, fast search, and relevant job discovery using modern web technologies.

Introduction

JobSphere addresses the increasing need for responsive and dynamic job search platforms. Unlike static job boards, JobSphere leverages Elasticsearch and modern frontend frameworks to provide real-time search experiences and rich filtering options, helping users find jobs that match their preferences more efficiently.

Literature Survey and Technologies Used

Modern job platforms like LinkedIn and Indeed use scalable backend technologies and intuitive UIs to handle large datasets and dynamic queries. JobSphere draws inspiration from these while focusing on performance and simplicity.

Technologies Used:

  • Frontend: Next.js with React Server Components
  • Backend: Elasticsearch
  • Styling: Tailwind CSS

Methodology

  • Frontend: Built with Next.js and React Server Components to support server-side filtering and fast rendering.
  • Backend: An Elasticsearch index stores job documents with fields like title, location, remote, role, salary, etc.
  • Query System: Uses full-text search with filters like date posted, experience level, and remote preference.
  • Architecture: Decoupled frontend and backend allow independent scaling and deployment.

Architecture Diagram

Results

  • Search queries return results in under 200ms on average.
  • Users can apply multiple filters simultaneously and see real-time updates.
  • Scalable architecture supports indexing thousands of job postings without performance degradation.

Filters

Stats

Companies

Future Scope

JobSphere demonstrates the power of combining modern frontend frameworks with scalable search infrastructure. Future enhancements include:

  • Integration with job provider APIs for real-time posting updates
  • User authentication and saved job alerts
  • Resume upload and AI-powered match scoring

Links

GitHub Repository

Mentors and Mentees Details

Mentor(s):

  • Agnirudra Sil
  • Yash Kumar Singh

Mentees:

  • Krishna
  • Navadeep Sai K
  • Reedham Shah
  • Sachin Rohra

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