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BUILDING SINCE 2016Español
Ernesto Vizcaíno Alvarado

Ernesto Vizcaíno Alvarado

Software Engineer & Founder

I build software from scratch and get it into people's hands.

I've built and launched software for small businesses, led engineering on fintech and AI products, and worked on machine learning for astronomical data. I care about the whole process: writing the code, shipping it, talking to users, and figuring out what to build next.

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Selected work

A few things I've built and put to the test.

Professional experience

I build products, ship them, and work on what happens after launch.

  • Apr 2026 to Present

    Independent Product Engineer / Founder

    Building and operating independent software products. I handle the engineering, deployment, integrations, payments, and the less glamorous work of finding users, measuring what happens, and improving the product after launch.

  • Oct 2025 to Jan 2026

    Aug 2026 to Sep 2026

    AI Engineer atXignus AI

    Built a credit preapproval model that helped SMBs access over MXN $4M in financing in its first month. Also built AI workflows using financial, banking, and tax data. Returned in 2026 to integrate CONTPAQi and automate accounting data pipelines and Excel reporting.

  • Jan 2024 to Jul 2025

    Technical Co-Founder atOliver AI

    Co-founded Oliver AI and led engineering of its fintech platform, from onboarding to lending workflows. Built AI agents with the Vercel AI SDK and AWS/BigQuery data pipelines for financial analysis, serving customers including Abaco Capital.

  • Aug 2021 to Dec 2023

    Founder / Engineer atOliver POS / ERP

    Built and launched a mobile point-of-sale app that reached 10,000+ downloads and 3,000 active users in three months. Later expanded it into a web-based ERP.

Research & Awards

  • 1st Place, Xólotl Hackathon

    Aug 2026

    Phase 3, Advanced Track · CUDI / LAMOD UNAM

    Rubin/LSST Astronomical Time Series Classification

    Won first place in the advanced track by building a pipeline to classify Rubin/LSST-like light curves and flag anomalies. Tested on 6,000 simulated curves from 600 observing cadences not seen during training. These results describe morphology classes, not physical star types.

    • 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 university recognition

    The award supported participation in CARLA 2026 in Córdoba, Argentina.

Research

  • 2026

    Gaia / OGLE Variable Star Classification

    Universidad Autónoma de San Luis Potosí

    Built a classifier for periodic variable stars using 491,073 Gaia sources and 91 engineered time-series, Fourier, color, and catalog features. Class-weighted XGBoost reached 0.9495 macro F1 on a held-out split; a separate comparison against mapped OGLE labels reached 0.9847 weighted F1.

Contact

Building something challenging? I'm interested in software engineering, applied AI, and early-stage teams. Tell me what you're working on.

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