AI-Driven Recruitment and Talent Acquisition in Hybrid Work Models: A Study of It Companies in Hyderabad

Authors

  • P. Pratheesha Assistant Professor, Anantha Lakshmi Institute of Technology and Sciences, Anantapur, AP, India
  • Chowlam Sandeep Kumar Associate Professor, Dr.K V Subba Reddy Institute of Management, Kurnool, AP, India

DOI:

https://doi.org/10.5281/zenodo.17340769

Keywords:

Artificial Intelligence, Talent Recruitment, Artificial intelligence Recruitment, Hybrid Work, Human Resource Technology, IT firm, Hyderabad, HR transformation, Employee experience

Abstract

There has been a high rate of shift to hybrid work models in the Information Technology (IT) sector in Hyderabad, which has transformed the traditional ways of recruiting and managing talents. Artificial Intelligence (AI) has been one of the critical facilitators of this change, automating the process of candidate sourcing; resume screening, skill evaluation, and virtual interviewing. This paper examines how the IT companies in Hyderabad are using AI-based recruitment tools to increase efficiency and decrease the hiring process and increase the quality of employees in a hybrid workplace setting. Other mentioned challenges in the research include algorithmic bias, issues of data privacy, and the necessity of human control in human-assisted decision-making with AI. The results show that AI can result in a higher level of operational efficiency and candidate experience, but in order to achieve this, it should be integrated strategically with human judgment and organizational culture. The paper concludes that the responsible deployment of AI can be a driver behind the development of nimble and future-oriented talent acquisition models in the IT industry of Hyderabad.

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Published

2025-10-13

How to Cite

Pratheesha, P., & Kumar, C. S. (2025). AI-Driven Recruitment and Talent Acquisition in Hybrid Work Models: A Study of It Companies in Hyderabad. International Journal of Science and Social Science Research, 3(3), 01–11. https://doi.org/10.5281/zenodo.17340769
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