Ai-Assisted HR Job Posting and Candidate Evaluation System Using Natural Language Understanding
G.Rama Rao,
R Sanjana,
V Shashank,
P.Udith Kumar,
P Jai Prakash
In recent years, recruitment processes have become increasingly complex due to the large volume of
job applications. To improve hiring efficiency and accuracy, researchers have proposed the use of
Artificial Intelligence and Natural Language Processing for automated resume screening. However,
traditional systems face challenges such as manual effort, time consumption, and inaccurate candidatejob
matching. To address these limitations, we propose an intelligent HR Job Posting System that
enhances recruitment using AI-driven techniques. Specifically, we first model the recruitment workflow
including resume submission, job description management, and candidate evaluation. We then design
a system that integrates web technologies, NLP, and machine learning algorithms to automate the
screening process. The proposed system leverages resume parsing to extract key details such as skills,
education, and experience, while job descriptions are analyzed to identify required competencies. It uses
advanced techniques like TF-IDF and BERT along with cosine similarity to compute matching scores
between resumes and job descriptions. Additionally, an AI assistant is incorporated to provide smart
recommendations and interactive support. We evaluated the system using real-time datasets, and the
results demonstrate improved accuracy and efficiency in candidate selection. The system is developed
using Python, Flask, MongoDB, and modern web technologies to ensure scalability and performance.
