Hirelink - A Job Hiring Platform

Abstract

HireLink is an innovative and user-focused job hiringplatform designed to transform the way job seekers and employers connect in today’s competitive world. Finding the right job or the right candidate is often a stressful and time-consuming process, filled with uncertainty and mismatched expectations. HireLink aims to simplify this journey by creating a more personalized, efficient, and human-centered hiring experience. The platform leverages smart matching technology to connect candidates with job opportunities that align with their skills, interests, and career goals. Instead of endlessly searching through listings, users receive tailored recommendations that make the job hunt more meaningful and less overwhelming. On the other side, employers benefit from an intuitive system that allows them to post job openings, manage applications, and identify the most suitable candidates quickly and effectively. What makes HireLink stand out is its focus on building genuine connections rather than just filling positions. The platform incorporates features such as real-time notifications, easy application tracking, and seamless communication tools, ensuring transparency and engagement throughout the hiring process. By reducing manual effort and enhancing decision-making with data-driven insights, HireLink helps both job seekers and recruiters save time while achieving better outcomes.

Introduction

HireLink, an intelligent recruitment platform designed to improve the modern hiring process using AI, real-time systems, and verification technologies.

Traditional job portals like LinkedIn and Naukri act mainly as job listings and suffer from issues such as irrelevant applications, fake resumes, lack of transparency, and poor communication between employers and candidates. HireLink aims to solve these problems by creating a more interactive, intelligent, and trustworthy recruitment system.

The platform introduces several key features, including:

• AI-based job matching based on skills and experience
• Real-time application tracking and notifications
• Employer and certificate verification using secure methods like SHA-256 hashing
• Resume parsing and scoring to extract and improve candidate profiles
• Fraud detection system to identify suspicious job posts
• Interactive dashboards for both job seekers and employers

The system is built using a full-stack architecture with React.js for the frontend, Node.js/Express.js for the backend, and MySQL + MongoDB for data storage. It also uses Socket.IO for real-time communication and AI techniques like TF-IDF and collaborative filtering for job recommendations.

The methodology includes resume parsing, fraud detection using weighted scoring, real-time notifications, and secure certificate verification. Employers can post jobs (which are checked for fraud), view applicants, and track hiring analytics, while job seekers receive personalized job recommendations and instant updates.

Research from related studies supports features such as ML-based fraud detection, NLP-based resume screening, collaborative filtering for recommendations, and real-time notification systems.

The results show strong performance:

• Fraud detection: ~92% accuracy
• Resume parsing: ~90% accuracy
• Real-time notifications: <500 ms latency
• Certificate verification: 100% tamper detection
• API response time: ~320 ms

Conclusion

HireLink successfully bridges the gap between job seekers and employers by offering an intelligent, transparent, and secure recruitment platform. By integrating AI?powered job matching, real?time notifications, and robust fraud detection, the system addresses the core frustrations of both sides—eliminating the “black hole” of applications and reducing the risk of fake postings. The hybrid matching engine improves recommendation relevance, while SHA?256 hashing ensures tamper?proof certificate verification. A multi?step employer verification process builds trust, and responsive dashboards provide meaningful insights for all users.

References

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Copyright

Copyright © 2026 Tejas Chavan, Shravani More, Payal Shirgire, Atharva Walam, Prof. Anita Yadav. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.