Recruiters now depend on applicant-tracking systems (ATS) that filter between 75% and 98% of résumés before a human review, often rejecting qualified graduates whose CVs lack machine-readable keywords. This automated gatekeeping disproportionately harms junior professionals in Artificial Intelligence (AI) across Mexico’s emerging tech hubs. VitaEX tackles the problem by shifting the ATS from gatekeeper to mentor, showing students what to add, why it matters, and how it aligns with real vacancies. The prototype is a three-tier web platform—React front-end, Django REST controller, MongoDB/PostgreSQL data layer—developed through the Spiral Model. A resilient Python scraper harvested LinkedIn postings from November 2024 to May 2025 for eight AI roles across five Mexican cities. Using the CRISP-DM framework, the team cleaned and normalized 5k+ vacancies, then mined more than 500,000 interpretable association rules with Apriori, creating a hybrid recommender that fuses rule confidence with SBERT cosine similarity. Beyond raw metrics, VitaEX delivers transparent explanations that boost ATS pass rates, raise skill awareness, and promote regional equity. This article synthesizes every major facet of the Spanish thesis—literature review, large-scale data collection, rule-based recommendation, and full-stack architecture—demonstrating how interpretable data-mining and modern web engineering can narrow the employability gap for AI graduates in developing economies.