AI-Driven Precision Recruitment Framework: Integrating NLP Screening, Advertisement Targeting, and Personalized Engagement for Ethical Technical Talent Acquisition

Abstract

This paper presents an integrated AI-driven precision recruitment framework addressing critical challenges in technical talent acquisition through coordinated application of artificial intelligence methodologies. The research establishes a comprehensive architecture integrating natural language processing for resume analysis, targeted advertisement optimization, and personalized candidate engagement within an ethically-governed system. The framework implements a three-tier structure comprising data ingestion, analytical processing, and decision support layers interconnected through privacy-preserving APIs. Experimental validation across multiple industry sectors demonstrates significant performance improvements, with average reductions of 36.6% in time-to-hire and 35.2% in recruitment costs. The NLP-based resume analysis component achieved 92.5% precision in qualification identification while the advertisement targeting mechanisms reduced cost-per-click by 48.3% on primary recruitment channels. Implementation of temporal graph neural networks for bias detection enabled a 73.2% reduction in evaluation disparities across protected characteristics. Longitudinal analysis indicates sustained performance improvements through continuous learning mechanisms, with particularly strong results in technology and financial services sectors. The research contributes to technical talent acquisition through establishing standards for bias detection and mitigation while demonstrating tangible commercial benefits of AI integration in human resource functions. The modular architecture enables adaptable implementation across organizational contexts with varying technical requirements and resource constraints.