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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM63144

SMARTHIRE: AI-POWERED PLACEMENT MANAGEMENT SYSTEM

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63144

Volume:

10 |

5 |

May 2026

Pages:

1-4

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM63144

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

Ayan Zuber Aalam

Ayan Zuber Aalam

Department of Computer Science and Engineering

Priyadarshini Bhagwati college of engineering

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Arshan Aasif Sheikh

Arshan Aasif Sheikh

Department of Computer Science and Engineering

Priyadarshini Bhagwati college of engineering

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Piyush Vinod Sontakke

Piyush Vinod Sontakke

Department of Computer Science and Engineering

Priyadarshini Bhagwati college of engineering

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Nilesh Omdeo Amnerkar

Nilesh Omdeo Amnerkar

Department of Computer Science and Engineering

Priyadarshini Bhagwati college of engineering

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Daksh Prashant Koli

Daksh Prashant Koli

Department of Computer Science and Engineering

Priyadarshini Bhagwati college of engineering

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Mentors

Prof. Anita S. Talmale

Prof. Anita S. Talmale

Department of Computer Science and Engineering

Priyadarshini Bhagwati college of engineering

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Abstract

In modern educational institutions, placement activities are still managed using spreadsheets, manual verification, and disconnected communication methods. These traditional processes create delays, increase administrative workload, and reduce placement transparency for students and placement officers. The proposed system, “SmartHire: AI-Powered Placement Management System,” is an intelligent platform designed to automate and simplify institutional placement operations through a centralized digital interface. The system acts as a complete placement assistant that helps students manage profiles, upload resumes, track applications, and prepare for interviews using Artificial Intelligence (AI). SmartHire is developed using Next.js for the frontend interface, FastAPI for backend processing, and Firebase Firestore for cloud database management. The platform also integrates Artificial Intelligence (AI), resume analysis, and applicant ranking systems to improve placement efficiency and student readiness. Students can interact with the system through role-based dashboards that provide job recommendations, analytics, notifications, and AI interview preparation. This intelligent platform minimizes manual placement activities, improves communication efficiency, and supports a smart digital campus environment for modern educational institutions.

KEYWORDS

Key Words: Placement Management System Artificial Intelligence Resume Analysis FastAPI Firebase Next.js Cloud Computing SaaS Smart Campus Automation

Cite This Article

Ayan Zuber Aalam, Arshan Aasif Sheikh, Piyush Vinod Sontakke, Nilesh Omdeo Amnerkar, Daksh Prashant Koli, Prof. Anita S. Talmale, "SMARTHIRE: AI-POWERED PLACEMENT MANAGEMENT SYSTEM," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-4, May 2026. https://doi.org/10.55041/IJSREM63144

Full Article/Conclusion

The development of SmartHire successfully addresses placement management challenges in educational institutions through automation, analytics, and Artificial Intelligence (AI). By integrating Next.js, FastAPI, Firebase Firestore, and AI-powered resume analysis, the platform provides an efficient solution for modern placement operations.  The implementation of AI-based resume analysis and interview preparation improves student placement readiness and reduces manual administrative workload. Furthermore, the analytical reports generated by the system improve institutional visibility and operational transparency.  In conclusion, SmartHire promotes a modern digital placement environment that improves communication, workflow management, and placement efficiency. Future work will focus on multi-tenant SaaS deployment, predictive placement analytics, and mobile application integration.

References

1.    Sommerville, I.: Software Engineering. 10th edn. Pearson Education, London (2015) 2.    Fowler, M.: Patterns of Enterprise Application Architecture. Addison-Wesley Professional, Boston (2002) 3.    Google Firebase Team: Firebase Documentation and Cloud Firestore Guide. Google Developers Documentation. Available at: https://firebase.google.com/ (2025) 4.    Ramírez, J., Patel, S., Kumar, A.: AI-Based Resume Screening and Applicant Ranking System. In: International Conference on Artificial Intelligence and Data Engineering. IEEE (2021) 112–118 5.    OpenAI: OpenAI API Documentation and GPT Models. Available at: https://platform.openai.com/docs/ (2025) 6.    Tiangolo, S.: FastAPI Framework Documentation. Available at: https://fastapi.tiangolo.com/ (2025) 7.    Vercel Team: Next.js Documentation for Full Stack Web Applications. Available at: https://nextjs.org/docs (2025) 8.    Pressman, R.S., Maxim, B.R.: Software Engineering: A Practitioner’s Approach. 8th edn. McGraw-Hill Education, New York (2019) 9.    Cloudinary Ltd.: Cloudinary Media Storage and Management Documentation. Available at: https://cloudinary.com/documentation (2025) 10. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)

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Publication Date

5/13/2026

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Review Rounds: 2

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