Hi, I'm
Junior Software Engineer moving intentionally toward Data, AI and Machine Learning Engineering.
Building a path from software delivery into data products,
ML pipelines, MLOps and
production-minded AI applications.
AWS Certified Cloud Practitioner
Credential
A software engineer with a data-first direction: building reliable systems, learning the ML lifecycle and turning domain problems into structured, testable products.
I bring a practical software base in corporate systems, APIs, Dynamics 365, C#/.NET, JavaScript, SQL Server, SSIS and Power BI, and I am channeling it into ML pipelines, model serving, MLOps and production-minded AI applications.
My previous professional path also gave me strong business-context exposure: I built Excel and Power BI reporting workflows for legal operations, worked with due diligence and operational documentation, worked with SCRUM routines, and used SAP ERP in a regulated financial environment.
My current trajectory is focused on Machine Learning Engineering, Data Engineering, AI systems, LLM/RAG applications and MLOps: clean pipelines, reproducible experiments, model APIs, cloud deployment and monitoring.
Current working level: junior software engineering experience with hands-on enterprise exposure, while building depth in Data, ML, AI and Cloud foundations.
A path from legal operations and reporting into corporate software engineering, now directed toward Data, AI and ML Engineering.
Atos
March 2026 - Present
CYMI do Brasil
June 2022 - July 2024
AgeRio
December 2018 - December 2020
Pyeongchang Organizing Committee for the 2018 Olympic & Paralympic Winter Games
January 2018 - February 2018
Public repositories, applied business work and learning artifacts that show the bridge between software engineering, data workflows and AI systems.
TypeScript MVP that monitors public Ticketmaster Brasil event pages and sends availability alerts. Built with Node.js, Express, React, Vite, configurable monitoring and optional Twilio WhatsApp notifications.
Property lease contract management system inspired by real operational pain. Includes Java/Spring API, React frontend, document generation, encrypted storage, audit logs, Docker and PostgreSQL-ready deployment.
Combined public learning track for Machine Learning Engineering and data practice: GitHub README organized around ML, Data Engineering, AI apps and MLOps, plus Kaggle as a lab for notebooks, EDA, baseline models and evaluation practice.
Current track: move from software delivery into production-minded ML systems, with strong data foundations, reproducible experiments and deployable AI applications.