International Journal of Research and Development in Engineering Sciences https://www.ijrdes.com K.S.Shaheena 7 2 (March - April) 2025 03 March-April enAcademic TalentsIndustry OpportunitiesHigher EducationGraduate SkillsWorkforce Alignment. Talent acquisition and placement are critical processes in human resource management yet they often face inefficiencies due to manual screening bias and mismatched hiring decisions Finding suitable candidates for an open role could be a daunting task especially when there are many applicants It can impede team progress for getting the right person on the right time In traditional models they use ML technologies like KNN and NLP on text based screen resuming which has limitations such as a bias inefficiency and lack of personality assessment To addressing this challenges we proposed a deep learning based framework for talent acquisition and placement using CNN and RNN Comparing to the traditional models this system enhances improving accuracy reduces recruiter workload to better workflows placement and talent acquisition This enhances efficiency improves decisionmaking and ensures optimal talent placement in organizations Copyright (c) 2026 International Journal of Research and Development in Engineering Sciences. All rights reserved. 2025 All Rights Reserved