| Summary: | Hundreds of resumes are received, processed, and managed by large companies and recruitment agencies. Furthermore, many people post their resumes on the internet. Organizations all across the world, on the other hand, are battling to locate the greatest resource. To complete this work, these organisations rely on industry expertise. Manual interaction is required in the resume screening process. Most of the current technologies search for keywords but do not consider semantics, resulting in many superfluous resumes being shortlisted. The goal of the proposed study is to create a smart resume screening algorithm that can automatically retrieve and process resumes. Name, phone / cell numbers, e-mail addresses, qualification, experience, skill sets, and other fields are mapped to the retrieved data. The proposed model uses AI and ML techniques to do so. The gathered data can be utilised to develop applicant profiles that meet the organization’s recruitment needs. By applying multiple filters to the data, an efficient man-less screening process can be achieved. The model is applied to the resumes that the company receives. The model has performed with an average accuracy of more than 90%. The model can be enhanced to apply on the resumes written in languages other than English.
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