TalentIQ: Intelligent Candidate Evaluation and Recruitment

DOI

10.2991/978-94-6239-723-1_42How to use a DOI?

Keywords

Recruitment Automation; Resume Parsing; Candidate Ranking; NLP; AI-Assisted Evaluation; FastAPI; Machine Learning Integration; MERN Stack; Interview Support System; Intelligent Hiring Plat-form

Abstract

The manual procedures of screening the resumes, sifting the applicants, and reviewing the outcome of the interview process in most organizations remain a part of the recruitment processes, thus making the process to be slower, less consistent and fair. To solve these issues, this paper will offer a solution, which is TalentIQ, a smart recruitment support solution that will facilitate the initial and intermediate stages of the hiring process. The system applies the traditional software engineering and AI-based modules with the aim of identifying the structured information concerning the resumes, determine the suitability of the candidate to the job position, and facilitate the interview processes. It relies on PDF text extraction, an NLP based processing phase and a rule based ranking mechanism to generate structured candidate evaluation. Auto-mated generation of questions and interpretation of answers to create a more homogeneous and interactive assessment space is also other AI-oriented features. The platform is implemented using MERN stack with microservices that are implemented using FastAPI in order to achieve modularity, scalability, and responsiveness. The preliminary findings of the implementation demonstrate that the processing speed, accuracy, and transparency have increased and it also helps to reduce the effort of the recruiter and enhance the hiring process.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Bhushan Chaudhari
AU  - Yashwant Chaudhari
AU  - Pranit Patil
AU  - Ishant Patil
AU  - Gaurav Patil
PY  - 2026
DA  - 2026/07/14
TI  - TalentIQ: Intelligent Candidate Evaluation and Recruitment
BT  - Proceedings of the International Conference on Responsible, Risk-aware, and Regulated AI (RRRAI 2026)
PB  - Atlantis Press
SP  - 473
EP  - 484
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-723-1_42
DO  - 10.2991/978-94-6239-723-1_42
ID  - Chaudhari2026
ER  -