Abstract:

In the face of growing volumes of job applications, traditional resume screening methods have become increasingly inefficient, error-prone, and biased. To address these challenges, this paper proposes a Cloud-Based Automated Resume Screening System that utilizes AWS Cloud services combined with Natural Language Processing (NLP) and machine learning algorithms to automate and enhance the recruitment process. The system automates the extraction of key information such as skills, qualifications, experience, and education from resumes, offering a comprehensive evaluation framework for recruiters. The architecture of the system is built on Amazon Web Services (AWS), which ensures high scalability, flexibility, and cost-efficiency. Core services such as Amazon S3 are used for storing resumes in various formats, while AWS Lambda enables serverless execution of preprocessing tasks and feature extraction. For model training and real-time resume evaluation, the system leverages Amazon SageMaker, which allows for the deployment of machine learning models at scale. Amazon Comprehend is employed for advanced text analysis, including entity extraction and sentiment analysis, to ensure a deeper understanding of the content within resumes.

Description:

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