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ML/AI Engineer focused on turning research into production-ready systems. I work across the full pipeline — from data processing and model training to optimization, deployment, and monitoring. My practice spans computer vision, NLP, LLM-based systems, and time series forecasting, always with a bias toward reproducibility and measurable impact. Currently building experience through end-to-end projects that bridge academic depth with engineering rigor. Open to remote ML/AI engineering roles — international or Brazil-based. |
name: Lucas Santos
role: ML / AI Engineer
domains:
- Computer Vision
- NLP & LLM Systems
- Time Series
- MLOps
education: Big Data (undergrad)
location: Brazil · Open to remote |
- Building a RAG-based Q&A system for agricultural document retrieval with vector search and local LLMs
- Developing a mobile CV app for real-time soil texture classification using on-device ML
- Exploring sentiment classification pipelines combining EDA, feature engineering, and ML models
| Project | Description | Stack |
|---|---|---|
| sb100_agents | RAG-based agricultural Q&A system — semantic retrieval from PDF knowledge bases with vector search and LLM-generated responses | FastAPI Qdrant Ollama RAG |
| visiosoil-app | Mobile app for real-time soil texture classification using on-device computer vision and machine learning | Flutter Dart OpenCV ML |
| tweet-sentiment-analysis | End-to-end NLP pipeline — exploratory data analysis and sentiment classification of Twitter data | Python scikit-learn NLP |
Core
Deep Learning
LLM & RAG
MLOps & Infra
