The widespread adoption of Applicant Tracking Systems (ATS) in modern recruitment has created a structural disadvantage for job seekers who lack professional writing expertise or familiarity with algorithmic screening criteria. While automated hiring tools have significantly improved the effi-ciency of candidate evaluation from the employer’s perspective, applicants continue to depend on static resume builders that provide formatting assistance without engaging with content quality. This paper proposes a conceptual framework for an AI-assisted resume generation platform that leverages the generative capabilities of Large Language Models (LLMs) to transform unstructured career data into structured, ATS-compatible pro-fessional documents. The proposed architecture integrates a structured user input layer, a role-specific prompt engineering pipeline, and a semantic document rendering engine. Unlike the prevailing focus of recruitment AI research—which centers on automated candidate screening—this work explicitly addresses the applicant’s document preparation challenge. The framework aims to reduce the cognitive effort associated with resume writing while improving structural consistency, semantic alignment with job descriptions, and overall document quality. Empirical evalu-ation through prototype implementation constitutes the primary direction of future work.