AI Engineer building RAG and agent systems. I work on the part after the model works: grounding, verification, and the limits that keep it honest.
Open to remote roles, international or Brazil-based · GMT-3
| What it is | Proof | Stack | |
|---|---|---|---|
| sb100_agents | Self-hostable RAG API that scores its own confidence | Agent gate calibrated to 96.7% / 3.3% TPR-FPR · 372 tests, 89.8% coverage · 15 ADRs | Python FastAPI Qdrant Ollama LangGraph |
| tweet-sentiment | Sentiment classification fine-tuned end to end | 0.887 macro F1, +51.9% over the frozen-features baseline · 28.5× faster Rust preprocessing, parity validated at 3 scales | Python Rust PyTorch Polars |
| weather-forecast | scikit-learn model running in the browser | Matched to 1e-6 against Python · 200-tree forest serialised and traversed in TypeScript · test-first across 20+ features, traceable in the log | Python TypeScript LightGBM |
| visiosoil-app | Soil texture from a photo, fully offline | 3rd of 1,300+ at FETEPS 2025 · paper published at ICPA/ConBAP 2026 · on-device inference in a killable isolate, group-aware dataset splits | Flutter Dart TFLite |
Closed source. Numbers verified against the repositories I contributed to.
| What it is | Proof | Stack | |
|---|---|---|---|
| Agent code-generation platform | Spec in, working application out | 103k lines, 3,397 tests · generated code isolated in per-job micro-VMs · deterministic security gate | Python LangGraph Kata Docker |
| AI advising platform | Grounded answers over institutional data | Hybrid retrieval built from scratch over pgvector · refuse-before-call gate and post-answer verifier · 854 tests | Python FastAPI pgvector Anthropic |



