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Hi, I'm Larissa 👋

Python Developer | Artificial Intelligence | Backend & Machine Learning

Building intelligent systems that combine backend engineering, APIs, and machine learning.


🎓 Education

B.Sc. in Computer Science - Estácio de Sá
Associate Degree in Artificial Intelligence - USCS (in progress)


💼 Experience

Python Developer | Artificial Intelligence Team at L5 Networks
Former IT Intern in IT Sales & After-Sales Systems at Mercedes-Benz Caminhões & Ônibus


🔗 Portfolio & Freelance Work

Freelance projects and portfolio:
https://lcp-leads.netlify.app/


About Me

I build intelligent systems that integrate backend engineering, APIs, and machine learning to solve real-world problems.

Currently working as a Python Developer in the Artificial Intelligence field, developing backend applications, REST APIs, and AI-driven solutions. My experience includes production systems, service integration, data processing, and machine learning applied to business scenarios.

I’m especially interested in AI Engineering, LLM integrations, intelligent automation, and scalable backend systems for data-driven applications.


Tech Stack

Artificial Intelligence & Machine Learning

Python · Scikit-learn · Pandas · NumPy · LLMs · NLP · Feature Engineering · Model Training · Model Inference · Model Evaluation

Backend & APIs

FastAPI · Flask · RESTful APIs · SQLAlchemy · API Development · Webservices · Backend Systems

Data Engineering & Databases

SQL · PostgreSQL · MySQL · SQLite · Data Processing · Data Pipelines · ETL

Systems & Integration

API Integration · System Integration · Intelligent Systems · Business Applications

Cloud & Tools

AWS · Docker · Git · Swagger · Linux

Frontend & Interfaces

Streamlit · React · TypeScript


Featured Projects

🥊 UFC Winner Predictor

End-to-end machine learning pipeline to predict UFC fight outcomes using pre-fight data.

Designed with comparative feature engineering and a strict temporal validation strategy to simulate real-world predictions. Benchmarked multiple models and achieved 74.7% accuracy and 0.824 ROC-AUC, with SHAP-based explainability and a deployed FastAPI inference service.

🔗 github.com/lacpavan/ufcpredictor · 🚀 Live demo


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