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Ernesto Vizcaíno

Ernesto Vizcaíno

AI & Full-Stack Engineer

I've been building software since I was 13. Since then, I've taken products from idea to thousands of users, built revenue-generating SaaS and fintech systems, and applied machine learning to millions of astronomical objects.

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Experience

I work across engineering and product, taking ideas from prototype to real users and production.

  • Oct 2025 to Feb 2026

    Aug 2026 to Sep 2026

    AI Engineer / Founding Team atFinanciamiento Inteligente / Xignus

    Designed the credit pre-approval model behind more than $4M MXN in SMB financing during its first month, alongside production AI agents and knowledge bases that bring tax, banking and financial data into credit workflows. Rejoined in 2026 to build CONTPAQi integrations and ETL pipelines that turn accounting data into automated Excel reports for reconciliation.

  • Aug 2021 to Jul 2025

    Technical Cofounder atOliver AI

    Started as a mobile POS for small businesses that reached 10,000+ downloads and 3,000 active users within three months, then grew into a web-based ERP. After customer discovery, I led the pivot to B2B fintech. I built a lending platform for financial institutions with production AI workflows for loan origination, risk review, compliance and document processing, plus AWS and BigQuery data pipelines for clients including Abaco Capital.

Awards

  • 1st Place, Xólotl Hackathon

    Aug 2026

    Phase 3, Advanced Track · CUDI / LAMOD UNAM

    Rubin/LSST Astronomical Time Series Classification

    Won the advanced track after building an uncertainty-aware classification and scientific prioritization pipeline for Rubin/LSST-like astronomical time series data.

    • 5.1M+ astronomical objects
    • 10 morphologies
    • 6,000 evaluation light curves
    • 600 completely unseen observing cadences
    • 68.47% balanced accuracy
    • 68.28% macro F1
    View official announcement

    Award includes attendance at CARLA 2026 in Córdoba, Argentina 🇦🇷.

Selected work

Selected products, systems and research I've built.

Research

  • 2026

    Gaia / OGLE Variable Star Classification

    Universidad Autónoma de San Luis Potosí

    Built a machine learning pipeline for periodic variable star classification, processing 491,073 Gaia sources and engineering 91 time series, Fourier, color and catalog features. A class-weighted XGBoost model reached 0.9495 macro F1 on held-out data and 0.9847 weighted F1 against mapped OGLE labels.

Stack

Contact

I'm open to AI and full-stack engineering roles, ambitious startup teams and select collaborations.

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