HirePrep: A Microservice-Based Integrated Placement Preparation Platform with AI Assistance

Year : 2026 | Volume : 13 | Issue : 01 | Page : 25 37
    By

    Sumit Madaan,

  • Rakshita Bhansali,

  • Nagendra Singh Sisodiya,

  • Lakshit Meena,

  • Sonam Lowry,

  1. Student, Department of Computer Science and Engineering, JECRC University, Jaipur, Rajasthan, India
  2. Student, Department of Computer Science and Engineering, JECRC University, Jaipur, Rajasthan, India
  3. Student, Department of Computer Science and Engineering, JECRC University, Jaipur, Rajasthan, India
  4. Student, Department of Computer Science and Engineering, JECRC University, Jaipur, Rajasthan, India
  5. Assistant Professor, Department of Computer Science and Engineering, JECRC University, Jaipur, Rajasthan, India

Abstract

Preparing for campus placements can be a confusing and time-consuming process. Students have to use different platforms for things like practice tests, study materials, talking to people, and getting updates from the administration. This is not a waste of time, but it also makes it harder for students to be productive and clear about what they need to do when they are getting ready for their careers. To make things better, we made something called HirePrep. It is a platform that brings together all the things students need to do to get ready for campus placements. HirePrep has important parts, including tests, managing resources, tracking progress, sending notifications, and controls for the administration. We used React to make the front end of the platform look nice and work well. Django and Spring Boot are used for the end. We use PostgreSQL to keep all the data safe and sound. The way HirePrep is built makes it easy to add things and scale up. Each part of the platform works independently. The other parts are communicating with each other using special codes. This makes the platform more stable and able to handle a lot of users. We also added a chatbot that uses intelligence to give students help when they need it. The chatbot is really good at understanding what students are asking. Gives the right answers most of the time. HirePrep shows that using small services can work well in schools and universities. It also gives an example of how to build a platform that can be used by a lot of people.

Keywords: Conversational AI, educational technology platform, microservice architecture, placement management system, student information system

[This article belongs to E-Commerce for Future & Trends ]

How to cite this article:
Sumit Madaan, Rakshita Bhansali, Nagendra Singh Sisodiya, Lakshit Meena, Sonam Lowry. HirePrep: A Microservice-Based Integrated Placement Preparation Platform with AI Assistance. E-Commerce for Future & Trends. 2026; 13(01):25-37.
How to cite this URL:
Sumit Madaan, Rakshita Bhansali, Nagendra Singh Sisodiya, Lakshit Meena, Sonam Lowry. HirePrep: A Microservice-Based Integrated Placement Preparation Platform with AI Assistance. E-Commerce for Future & Trends. 2026; 13(01):25-37. Available from: https://journals.stmjournals.com/ecft/article=2026/view=242224


References

  1. Balakrishnan S, Bargavi N. An in-depth review on campus recruitment and the challenges faced. Int J Indian Cult Bus Manag. 2025;34(4):429–42. doi:10.1504/IJICBM.2025.145681.
  2. Nickerson JV. Teaching the integration of information systems technologies. IEEE Trans Educ. 2006;49(2):271–7. doi:10.1109/TE.2006.873966.
  3. Tang A, Avgeriou P, Jansen A, Capilla R, Ali Babar MA. A comparative study of architecture knowledge management tools. J Syst Softw. 2010;83(3):352–70. doi:10.1016/j.jss.2009.08.032.
  4. Nadareishvili I, Mitra R, McLarty M, Amundsen M. Microservice Architecture: Aligning Principles, Practices, and Culture. Sebastopol (CA): O’Reilly Media; 2016.
  5. Velepucha V, Flores P. A survey on microservices architecture: Principles, patterns and migration challenges. IEEE Access. 2023;11:88339–58. doi:10.1109/ACCESS.2023.3305687.
  6. Alfehaid A, Hammami MA. Artificial intelligence in education: Literature review on the role of conversational agents in improving learning experience. Int J Membr Sci Technol. 2023;10(3):3121–9. doi:10.15379/ijmst.v10i3.3045.
  7. Mew L. Information systems education: The case for the academic cloud. Inf Syst Educ J. 2016;14(5):71–9.
  8. Das S, Dayal M. Exploring determinants of cloud-based enterprise resource planning selection and adoption. J Inf Technol Case Appl Res. 2016;18(1):11–36. doi:10.1080/15228053.2016.1160733.
  9. Riad AM, El-Ghareeb HA. A service oriented architecture to integrate mobile assessment in learning management systems. Turk Online J Distance Educ. 2008;9(2):200–19.
  10. Richardson C. Microservices Patterns: With Examples in Java. New York (NY): Simon & Schuster; 2018.
  11. Newman S. Building Microservices: Designing Fine-Grained Systems. Sebastopol (CA): O’Reilly Media; 2021.
  12. Yin Z, Liu J, Chen B, Chen C. A delivery robot cloud platform based on microservice. J Robot. 2021;2021:1–10. doi:10.1155/2021/6656912.
  13. Lyu Z, Wei H, Bai X, Lian C. Microservice-based architecture for an energy management system. IEEE Syst J. 2020;14(4):5061–72. doi:10.1109/JSYST.2020.2981095.
  14. Winkler R, Söllner M. Unleashing the potential of chatbots in education: A state-of-the-art analysis. Acad Manag Proc. 2018;2018:15903. doi:10.5465/AMBPP.2018.15903abstract.
  15. Pérez JQ, Daradoumis T, Puig JMM. Rediscovering the use of chatbots in education: A systematic literature review. Comput Appl Eng Educ. 2020;28(6):1549–65. doi:10.1002/cae.22326.
  16. Okonkwo CW, Ade-Ibijola A. Chatbots applications in education: A systematic review. Comput Educ Artif Intell. 2021;2:100033. doi:10.1016/j.caeai.2021.100033.
  17. Aldiab A, Chowdhury H, Kootsookos A, Alam F, Allhibi H. Utilization of learning management systems in higher education: A case review for Saudi Arabia. Energy Procedia. 2019;160:731–7. doi:10.1016/j.egypro.2019.02.186.
  18. Bhamangol P, Ningappa B, Nandavadekar DV, Khilari P, Hanmant S. Enterprise resource planning system in higher education: A literature review. Int J Manag Res Dev. 2011;1(1):1–7.
  19. Fowler M, Lewis J. (2014). Microservices: A definition of this new architectural term [Online]. Martin Fowler. Thoughtworks. Scientific Research Publishing. Available from: https://martinfowler.com/articles/microservices.html
  20. Evans E. Domain-Driven Design: Tackling Complexity in the Heart of Software. Boston (MA): Addison-Wesley; 2004.
  21. Mazzara M, Meyer B. Present and Ulterior Software Engineering. Cham: Springer; 2017. doi:10.1007/978-3-319-67425-4.
  22. Di Francesco P, Malavolta I, Lago P. Research on architecting microservices: Trends and focus. 2017 IEEE International Conference on Software Architecture (ICSA), Gothenburg, Sweden. 2017. p. 21–30. doi:10.1109/ICSA.2017.24.
  23. Nygard M. Release It!: Design and Deploy Production-Ready Software. 2nd ed. Raleigh (NC): Pragmatic Bookshelf; 2018.
  24. Django Project. (2026). Django Software Foundation. [Online]. Django (The web framework for perfectionists with deadlines). Available from: https://www.djangoproject.com/
  25. Spring Boot. 4.0.5. [Online]. Spring. Available from: https://spring.io/projects/spring-boot
  26. Facebook Open Source. (2021). React. React – a JavaScript library for building user interfaces [Online] Meta Platforms, Inc. Available from: https://legacy.reactjs.org/
  27. Spring Cloud Gateway. (2017). Spring Cloud Gateway. 4.0.9. [Online]. Spring.io. Available from: https://docs.spring.io/spring-cloud-gateway/docs/current/reference/html/
  28. The PostgreSQL Global Development Group. (2026). PostgreSQL. [online] PostgreSQL. Available from: https://www.postgresql.org/
  29. Deshpande Y, Hansen S. Web engineering: Creating a discipline among disciplines. IEEE Multimed. 2001;8(2):82–7. doi:10.1109/93.917974.
  30. Fielding RT. Architectural styles and the design of network-based software architectures [PhD thesis]. Irvine (CA): University of California; 2000.
  31. Wolf T, Debut L, Sanh V, Chaumond J, Delangue C, Moi A, et al. Transformers: State-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations; 2020 Oct; Online. Stroudsburg (PA): Association for Computational Linguistics; 2020. p. 38–45. doi:10.18653/v1/2020.emnlp-demos.6.
  32. Devlin J, Chang MW, Lee K, Toutanova K. BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies; 2019 Jun; Minneapolis (MN). Stroudsburg (PA): Association for Computational Linguistics; 2019. p. 4171–86. doi:10.18653/v1/N19-1423.
  33. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems; 2017 Dec 4–9; Long Beach (CA). Red Hook (NY): Curran Associates Inc.; 2017. p. 6000–10.
  34. Wilde E, Pautasso C. REST: From Research to Practice. New York (NY): Springer; 2011.
  35. Crockford D. The application/json media type for JavaScript Object Notation (JSON). Request for Comments: 4627. Fremont (CA): RFC Editor, Network Working Group; 2006. doi:10.17487/RFC4627.
  36. Masse M. REST API Design Rulebook: Designing Consistent RESTful Web Service Interfaces. Sebastopol (CA): O’Reilly Media; 2011.
  37. Hardt D. The OAuth 2.0 authorization framework. RFC 6749. Request for Comments. 2012 Oct. doi:10.17487/RFC6749.
  38. Hu VC, Ferraiolo D, Kuhn R, Schnitzer A, Sandlin K, Miller R, et al. Guide to attribute based access control (ABAC): definition and considerations. NIST Spec Publ 800-162. Gaithersburg (MD): National Institute of Standards and Technology; 2014 Jan. doi:10.6028/NIST.SP.800-162.
  39. Jones M, Bradley J, Sakimura N. JSON Web Token (JWT). RFC 7519. Request for Comments. 2015 May. doi:10.17487/RFC7519.
  40. Sheffer Y, Holz R, Saint-Andre P. Recommendations for secure use of Transport Layer Security (TLS) and Datagram Transport Layer Security (DTLS). RFC 7525. Request for Comments; 2015 May. doi:10.17487/RFC7525.
  41. Schneier B. Applied Cryptography: Protocols, Algorithms, and Source Code in C. New York (NY): John Wiley & Sons; 2007.
  42. Merkle RC. Protocols for public key cryptosystems. In: Simmons G, editor. Secure Communications and Asymmetric Cryptosystems. New York (NY): Routledge; 1983. p. 73–104. doi:10.4324/9780429305634.
  43. Pahl C, Jamshidi P. Microservices: A systematic mapping study. In: Proceedings of the 6th International Conference on Cloud Computing and Services Science (CLOSER); 2016; Rome, Italy. Setúbal: SciTePress; 2016. p. 137–46. doi:10.5220/0005785501370146.
  44. Bharti SK, Babu KS, Jena SK. Parsing-based sarcasm sentiment recognition in Twitter data. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining; 2015 Aug 25–28; Paris, France. New York (NY): Association for Computing Machinery; 2015. p. 1373–80. doi:10.1145/2808797.2808910.
  45. Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. Computer. 2009;42(8):30–7. doi:10.1109/MC.2009.263.

Regular Issue Subscription Original Research
Volume 13
Issue 01
Received 11/02/2026
Accepted 24/02/2026
Published 31/03/2026
Publication Time 48 Days


Login


My IP

PlumX Metrics