Recruitment is a critical yet resource-intensive process in organizations across industries. Traditional resume screening relies on manual human review, which is time-consuming and prone to subjective biases. This paper presents the Hiring Agent, an AI-driven system that automates and objectifies the resume evaluation process by combining large language models (LLMs), document processing, and external data augmentation. The system parses resume PDFs into machine-readable formats, extracts structured data using local or cloud-based LLMs, augments candidate information with GitHub profile signals, and produces objective evaluations with quantified scores across multiple dimensions. We demonstrate the system's architecture, implementation details, and discuss its effectiveness in reducing recruitment friction while maintaining evaluation quality. The proposed system supports both local deployment via Ollama and cloud-based inference through Google Gemini, providing flexibility in deployment scenarios. Our evaluation shows that the Hiring Agent can effectively synthesize multiple data sources to produce comprehensive candidate assessments suitable for initial screening and comparative ranking.
Artificial Intelligence, Large Language Models, Recruitment, Resume Processing, Natural Language Processing, Candidate Evaluation, GitHub Integration.
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