I'm a Software Engineer specializing in Python, focused on building robust data solutions and operationalizing Machine Learning models (MLOps). Experienced in ETL pipelines, creating RESTful APIs with FastAPI, and implementing generative AI systems using LangChain and LLMs.
I enjoy solving complex problems in cloud environments (AWS/GCP) while ensuring scalability and efficiency. My work spans multiple disciplines, from fullstack web development to AI-driven data analytics.
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A production-ready MLOps pipeline serving a GPT-2 model via a RESTful API (Flask), fully containerized with Docker. Code quality and integrity are ensured via CI/CD with GitHub Actions and unit tests (Pytest).
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An interactive task management dashboard built with React (TypeScript) frontend and a robust Python backend. Fully responsive and designed for real-time operations management, demonstrating end-to-end system architecture.
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An AI assistant integrating text generation with GPT, context analysis, and audio output via Text-to-Speech (TTS). The project uses modern NLP techniques for interactive, functional conversation.
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A web-based game in pure JavaScript, evolved from a study project to a responsive application with multiple difficulty levels, sound effects, and a CI/CD pipeline via GitHub Actions to ensure quality and integrity.
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A full-featured finance management dashboard, showcasing real-time analytics, interactive charts, and data integration from multiple sources. Built with React + Python for a complete fullstack experience. |
A cross-platform Scratch-based game for Windows and Linux, demonstrating gamification and interactive design. A multidisciplinary project combining programming logic, design, and user interaction. |
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A professional certification project demonstrating an end-to-end data science workflow: from data extraction with Python & SQL to analysis, modeling, and machine learning deployment. This project validates my IBM Data Science Professional Certification. |
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| Categoria | Tecnologias |
|---|---|
 Python & Core |
Python, Docker, Git, CI/CD (GitHub Actions), Pytest, APIs REST |
 IA & Machine Learning |
TensorFlow, PyTorch, Transformers, LangChain, Scikit-learn, Pandas, NLP |
 Backend & Bases de Dados |
FastAPI, Flask, SQL (PostgreSQL), NoSQL (MongoDB), Node.js |
 Cloud & Big Data |
AWS, GCP, Spark, Apache Airflow |
 Front-End & Visualização |
JavaScript, React, TypeScript, HTML5, CSS3, Chart.js, Power BI |
My career combines a strong foundation in Software Engineering with a perspective in Finance. This enables me to build robust AI and data solutions aligned with strategic objectives. I transform complex data into practical, scalable applications that generate real value.
| Pillar | Description |
|---|---|
| Current Focus | Python, MLOps, and Generative AI. Hands-on experience in end-to-end pipelines from model creation to deployment with APIs and containers. |
| Business Insight | Leveraging Finance and Data Analysis background to build dashboards and BI solutions translating technical metrics into actionable insights. |
| Strong Foundations | Over a decade in technology, starting with Web Development and Systems Analysis, providing resilient and versatile engineering foundations. |
| Lifelong Learning | Continuous learning (lifelong learning) to innovate and combine knowledge from multiple fields for creative, efficient solutions. |
| Program / Degree | Institution & Period | Focus | |
|---|---|---|---|
| Postgraduate in Big Data | Univitória | 2025-2026 (Ongoing) | Big Data, Hadoop, Spark, AI & Machine Learning |
| Postgraduate in Software Engineering | Faculdade Focus | 2023 | Agile Methods, IT Governance, Systems Analysis |
| Data Analysis & Systems | UNOPAR | 2022 | SQL, Data Modeling & Analysis |
| Category | Details |
|---|---|
| Official IBM Certifications | • Data Science Professional Certificate • Linux Commands and Shell |
| Languages | • Portuguese (Native) • English (Fluent) • Spanish (Fluent) |
| Focus Area | Details |
|---|---|
| High-Impact Python & AI Specialist | •  Processing Performance: 1TB data processed in <1 hour using PySpark.•  Automation Efficiency: Scripts 40% faster in Bash/Python.• AI Model Accuracy: 90% accuracy in NLP/CV models; 30% cost reduction via automation.•  Tool Mastery: Proficient in 50+ tools: AWS, Docker, TensorFlow, Tesseract OCR. |
| Recognition | "IBM-certified professionals in the Top 5% deliver 15% infrastructure cost savings on average." Sources: Stack Overflow 2023, IEEE, Linux Foundation |
- Most Used Languages (Data Science Focus)
- Most Used Languages (Fullstack Focus)
Based on insights from the GitClear study (2020-2024), which analyzed 878,592 developer-years, my productivity approach focuses on commit frequency as a more stable and reliable indicator than lines of code. Commits reflect a true measure of activity and consistent progress.
With 889 contributions in the last year, my profile aligns with high-performing developers, demonstrating a steady acceleration toward the Top 30% benchmark (1,000+ commits/year).
The global productivity spectrum reveals the following distribution:
| Percentile | Commits per Year | Activity Level |
|---|---|---|
| Top 1% | 4,307 | Elite |
| Top 10% | 1,647 | High Performance |
| Median | 417 | Active Developer |
| Below Average | 130 | Occasional |
Professionals who exceed 1,000 commits annually exhibit distinct habits and metrics:
Median Commits: 1,563/year (~6.5 per workday).
Active Days: Between 200-250 days per year.
AI Impact: Tools like Copilot are increasing the volume of changes (Diff Delta) per commit, optimizing workflows rather than just inflating commit counts.
This philosophy guides my daily work, focusing on consistent and measurable value delivery.
