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The present research introduces a novel integrated platform to transform the IT industry’s recruiting process by streamlining talent acquisition procedures. The platform tackles significant challenges through three core innovations: (1) an Applicant Tracking System (ATS) with customizable workflows for precise candidate-job matching, (2) a cheating detection mechanism achieving 95% accuracy in identifying malpractice during assessments, and (3) a dedicated interview management module optimizing scheduling and evaluations. Unlike prior systems (Peicheva in Data analysis from the applicant tracking system, [2] and Garg et al. in Review paper: role of artificial intelligence in recruitment process, [4]), our framework integrates role-based security and fragmented workflows into a unified platform, addressing gaps in fairness and scalability. We have implemented AES-256 encryption and role-based access control to ensure GDPR-compliant security and scalability. Cross-enterprise testing demonstrated a 30% average recruiting lifecycle reduction and 4.8/5 user satisfaction for ease of use. Though the platform raises the integrity of the assessment and improves operational efficiency, behavioural characteristic analysis and limitations in mitigating AI bias are acknowledged. This contribution provides a secure, scalable framework for current recruitment, optimizing between automation and equity.

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Designing and Developing Comprehensive Framework with Improved Insights for Optimizing Talent Acquisition Process of Engineering Graduates

  • 会议论文

  • 发表日期：

    2026年

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  • Anurag Sawant,
  • Shravandeep Yadav,
  • Abdul Hadi Shah,
  • Uttam Kolekar,
  • Sonal Balpande

摘要

The present research introduces a novel integrated platform to transform the IT industry’s recruiting process by streamlining talent acquisition procedures. The platform tackles significant challenges through three core innovations: (1) an Applicant Tracking System (ATS) with customizable workflows for precise candidate-job matching, (2) a cheating detection mechanism achieving 95% accuracy in identifying malpractice during assessments, and (3) a dedicated interview management module optimizing scheduling and evaluations. Unlike prior systems (Peicheva in Data analysis from the applicant tracking system, [2] and Garg et al. in Review paper: role of artificial intelligence in recruitment process, [4]), our framework integrates role-based security and fragmented workflows into a unified platform, addressing gaps in fairness and scalability. We have implemented AES-256 encryption and role-based access control to ensure GDPR-compliant security and scalability. Cross-enterprise testing demonstrated a 30% average recruiting lifecycle reduction and 4.8/5 user satisfaction for ease of use. Though the platform raises the integrity of the assessment and improves operational efficiency, behavioural characteristic analysis and limitations in mitigating AI bias are acknowledged. This contribution provides a secure, scalable framework for current recruitment, optimizing between automation and equity.