^{Machine Learning Engineer · Computer Vision · Healthcare AI · Production ML}

Computer Science undergraduate at UFPB and Machine Learning Engineer working on Computer Vision, Healthcare AI, and Agentic RAG systems.

My focus is building end-to-end machine learning systems that move beyond experimentation and deliver reliable performance in real-world environments.

Current interests include:

• Computer Vision and Object Tracking
• Healthcare AI
• Multimodal Systems
• Agentic AI and RAG
• MLOps and Production ML
• High-Performance Computing with C++

From research prototypes to production-grade AI systems.

• Developed surgical sponge tracking pipelines achieving 92% tracking accuracy
• Built agentic RAG systems for breast cancer diagnosis reaching 85% diagnostic accuracy
• Generated 10,000+ synthetic clinical samples for healthcare AI research
• Improved computer vision classification performance by 60% over baseline approaches
• Presented AI research results to medical professionals and technical stakeholders
• Contributed to research and engineering projects spanning healthcare, sports analytics, and machine learning infrastructure

• Computer Vision (Detection, Tracking, Classification)
• Agentic AI Systems
• Retrieval-Augmented Generation (RAG)
• Synthetic Data Generation
• Time Series and Sequential Modeling
• Deep Learning with PyTorch
• FastAPI-based ML Services
• MLOps and Dockerized Deployments
• Performance-Oriented C++ Development

AI Engineer & Research Intern | 2025–Present

Working on healthcare AI and computer vision systems in collaboration with academic and industry partners.

Projects include:

• Surgical sponge tracking using ByteTrack and Hidden Markov Models
• Agentic RAG systems for breast cancer diagnosis
• Synthetic data generation pipelines for medical AI
• End-to-end machine learning infrastructure for research-to-production workflows

Machine Learning Engineer | 2025–Present

Developing applied machine learning solutions for real-world analytics and decision support.

Projects include:

• Real-time sports analytics systems
• CLIP fine-tuning for jersey color classification
• Audio similarity systems using Siamese Neural Networks
• Model evaluation, monitoring, and performance analysis

Researcher | 2022–2023

• Research in Partial Differential Equations and Geometric Analysis
• Awarded Young Researcher distinction at UFPB research symposium

Minimal neural network framework built entirely from scratch.

• Backpropagation
• Stochastic Gradient Descent
• Matrix Operations
• Multiple Activation Functions
• XOR Learning Demonstration

Agentic retrieval-augmented system for clinical decision support.

• FAISS Vector Database
• LLM-based Reasoning
• Diagnostic Workflow Design
• Synthetic Medical Data Generation

Computer vision pipeline for surgical environments.

• Object Detection
• Multi-Object Tracking
• ByteTrack
• Hidden Markov Models
• Occlusion Handling

Deep learning system for facial emotion classification.

• CNN Architecture
• Data Preprocessing Pipelines
• Error Analysis
• Performance Visualization

Python · SQL · C/C++ · Java · TypeScript

PyTorch · TorchVision · Scikit-Learn · XGBoost

LangChain · FAISS · Agentic Workflows

FastAPI · Docker · Git · CI/CD · Linux

Pandas · Matplotlib · Power BI

• Fundação Estudar Leadership Program Fellow (2026)
• CAPES Talento Universitário (2025) — Top 5%
• Young Researcher Award — XXXI ENIC/UFPB (2023)

  

Building machine learning systems that are reliable enough for real-world decisions.