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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM63916

AI POWERED JOB MATCHING SYSTEM

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63916

Volume:

10 |

5 |

May 2026

Pages:

1-10

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM63916

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

AD

Asmita Dahule

Computer Science and Engineering

Government College of Engineering Chandrapur

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SB

Snehal Borkute

Computer Science and Engineering

Government College of Engineering Chandrapur

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AD

Aamisha Daf

Computer Science and Engineering

Government College of Engineering Chandrapur

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SJ

Shweta Janwe

Computer Science and Engineering

Government College of Engineering Chandrapur

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DU

Dhanashri Ughade

Computer Science and Engineering

Government College of Engineering Chandrapur

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Mentors

PSG

Prof. S.U. Ghumbare

Computer Science and Engineering

Government College of Engineering Chandrapur

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Abstract

The AI-Powered Job Matching System is a smart recruitment platform developed using the MERN stack (MongoDB, Express.js, React.js, and Node.js). The system aims to improve the hiring process by matching job seekers with suitable job opportunities based on their skills, qualifications, and interests. Traditional job portals often provide irrelevant recommendations and require manual screening, which consumes time and effort. This platform uses Artificial Intelligence and Machine Learning techniques to analyze resumes and job descriptions for accurate job recommendations. Recruiters can easily manage job postings and filter candidates, while users receive personalized job suggestions and notifications. The system also includes secure authentication, resume upload, and profile management features. The proposed solution increases recruitment efficiency, reduces job mismatch, and provides a user-friendly experience for both employers and applicants. It offers a modern, scalable, and intelligent approach to online recruitment systems.

KEYWORDS

AI Powered

Cite This Article

Asmita Dahule, Snehal Borkute, Aamisha Daf, Shweta Janwe, Dhanashri Ughade, Prof. S.U. Ghumbare, "AI POWERED JOB MATCHING SYSTEM," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-10, May 2026. https://doi.org/10.55041/IJSREM63916

Full Article/Conclusion

The AI-Powered Job Matching System developed using the MERN stack provides an efficient and intelligent solution for modern recruitment processes. The system successfully connects job seekers and recruiters through AI-based job recommendations and smart candidate matching. It simplifies the hiring process by reducing manual effort, saving time, and improving the accuracy of job matching. The platform offers important features such as user authentication, resume upload, job posting, personalized job recommendations, and application management. The React.js frontend provides a responsive and user-friendly interface, while Node.js, Express.js, and MongoDB ensure secure data handling and smooth system performance. The integration of Artificial Intelligence and Machine Learning improves recruitment efficiency by analyzing user skills, qualifications, and job requirements. The system is scalable, easy to maintain, and suitable for future enhancements. Overall, the project achieves its objective of providing a smart, secure, and user-friendly online recruitment platform that benefits both recruiters and job seekers.

References

1]        Paparrizos, K., Cambazoglu, B. B., & Gionis, A., “Machine Learned Job Recommendation,” in ACM Conference on Recommender Systems, Chicago, USA, 2011, pp. 325–328.   2]        Malinowski, J., Keim, T., Wendt, O., & Weitzel, T., “Matching People and Jobs: A Bilateral Recommendation Approach,” in Hawaii International Conference on System Sciences, 2006.   3]        Sharma, A., & Patel, R., “Skill-Based Intelligent Job Recommendation Using Machine Learning,” International Journal of Computer Applications, vol. 183, no. 42, pp. 15–21, 2021.   4]        Kumar, M., & Singh, P., “AI-Based Recruitment and Resume Analysis System,” International Research Journal of Engineering and Technology (IRJET), vol. 9, no. 6, pp. 1023–1028, 2022.   5]        LinkedIn, “AI-Powered Talent Matching and Recruitment Solutions,” 2024. Available: LinkedIn Talent Solutions

Editorial & Review Transparency

Publication Date

5/24/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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Conflict of Interest

The authors declare no conflict of interest related to this research work.

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