Gabriel Silva Gouvêa

Software Engineer

Hey, I'm Gabriel Silva Gouvêa, a Software Engineer currently working at MOVEdot AI (YC F25). I love solving problems, and to me, coding is just a tool to do so. My personal goals are to spread the gospel of Jesus Christ, be an awesome husband and future father, and become one of the top 1% software engineers in the world. Welcome to my portfolio!

About Me Experience Projects Contact

About Me

Get to know me better

Gabriel Silva Gouvêa

Gabriel Silva Gouvêa

Software Engineer

Rio de Janeiro, RJ

Hey, I'm Gabriel Silva Gouvêa, a Software Engineer currently working at MOVEdot AI (YC F25). I love solving problems, and to me, coding is just a tool to do so. I specialize in AI-driven solutions, full-stack development, and motorsport data analysis, building multi-agent orchestration systems that help race teams analyze performance data in real-time.

My experience spans from building award-winning educational AI platforms at Realms IP.TV to developing telemetry systems for Formula SAE racing teams. I specialize in JavaScript/TypeScript, Python, and modern web technologies, with a strong focus on creating scalable, maintainable applications.

My personal goals are to spread the gospel of Jesus Christ, be an awesome husband and future father, and become one of the top 1% software engineers in the world. I'm fluent in both Portuguese and English, and I'm passionate about using technology to solve complex problems and make a positive impact in the world of motorsports and education.

Experience

A timeline of my professional journey

Software Engineer at MOVEdot AI (YC F25)

May 2025 – Present

  • Designed and maintained the company's core multi-agent orchestration system for motorsport data analysis (Stint/Deep Analysis Agents), enabling race teams to cut lap analysis time from hours to minutes.
  • Integrated assistant-ui and custom canvas interface to provide real-time performance insights to clients including IndyCar teams.
  • Built a manual video synchronization tool using AWS Lambda, MediaConvert, and S3, improving data alignment across race events.

Software Engineer at Realms IP.TV

Jan. 2023 – Apr. 2025

  • Led the development of EduxGen.AI, an award-winning AI educational material generator (backend LLM, websockets, FFmpeg and frontend UI). 2025 Bett Awards finalist for "AI for Teaching and Assessment" and "Tech & Learning Awards of Excellence 2024" Winner.
  • Designed CGS (Content Generation System), a new and improved backend for EduxGen.AI with custom agent orchestration and enhanced scalability. Worked on an in-house websockets engine.
  • Developed and optimized Persona-Knowledge (Python RAG System) using Neo4j and PGVector, significantly reducing Docker image size and build time by 70%.
  • Integrated AWS S3 and implemented CI/CD best practices for improved deployment efficiency.

Data Acquisition Engineer - Icarus Formula SAE

May 2020 – Jun. 2021 / Jan. 2022 - Sep. 2022

  • Developed a real-time vehicle telemetry system with C++ microcontroller routines and a C# live data visualization application.
  • Managed a team and 12 projects, implementing agile methodologies to improve delivery and productivity as Power train Manager.
Download Resume (PDF)

My Projects

A collection of my work and personal endeavors in AI, motorsport technology, and software development.

AI Agent Infrastructure

MOVEdot AI Multi-Agent System

LangGraph Python AWS

Designed and maintained the company's core multi-agent orchestration system for motorsport data analysis, enabling race teams to cut lap analysis time from hours to minutes.

Educational AI Platform

EduxGen.AI

Node.js WebSockets FFmpeg

Award-winning AI educational material generator with backend LLM, websockets, FFmpeg and frontend UI. 2025 Bett Awards finalist and Tech & Learning Awards of Excellence 2024 Winner.

Racing Telemetry

Formula SAE Telemetry System

C++ C# Microcontroller

Developed a real-time vehicle telemetry system with C++ microcontroller routines and a C# live data visualization application for Formula SAE racing team.

RAG System

Persona-Knowledge RAG System

Python Neo4j PGVector

Developed and optimized a Python RAG system using Neo4j and PGVector, significantly reducing Docker image size and build time by 70%.

Get in Touch

I'm always open to discussing new projects, creative ideas, or opportunities to be part of your visions. Feel free to reach out!

Contact Information

Let's connect and build something amazing together.

mail gabrielgouvea@poli.ufrj.br LinkedIn GitHub

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