International Journal of Science, Engineering and TechnologyAuthors: Mohit Pundir
Abstract: The integration of Artificial Intelligence (AI) in Human Resources (HR) and talent ac-quisition has transitioned from an experimental phase to an operational necessity, driven primarily by the proliferation of Large Language Models (LLMs) and advanced machine learning algorithms. While AI introduces unprecedented efficiency in recruitment automa-tion, it simultaneously risks eroding organizational trust and the interpretability of HR systems. This paper presents a comprehensive, multi-layered approach to modernizing HR infrastructure without sacrificing human-centric decision-making. First, the paper exam-ines the macroeconomic impacts of AI-driven recruitment automation, highlighting shifts in hiring cycles, cost reductions, and global adoption trends. Second, an offline, Natural Language Processing (NLP) based resume parsing framework is proposed, utilizing tools such as spaCy and regex to structure applicant data efficiently, demonstrating an over-all accuracy of 96.0%. Finally, to mitigate the systemic risks of algorithmic black boxes and judgment inflation, the paper introduces Retrieval-Augmented Generation (RAG) as a foundational cybernetic design pattern for HR. By separating memory from generation, the RAG framework ensures that AI acts as an explainability interface and a sensemaking copilot rather than an autonomous decision-maker. Through this synthesis, the paper ar-gues that the ultimate goal of AI in HR is not merely the automation of transactions, but the architectural preservation of dignity, narrative consistency, and institutional memory.
