AI/ML Systems Engineer. I build, test, and operate AI/ML systems with reproducible releases and documented limitations.
I build, test, and operate AI/ML systems with emphasis on:
AI/ML Systems:
- RAG (Retrieval-Augmented Generation) and retrieval workflows
- LLM orchestration and tool calling
Backend & Delivery:
- Python, FastAPI, and TypeScript
- CI/CD and reproducible releases
- Structured logging and observability
- Atlas.WM v4.0.1 · CI — Research baseline; limitations documented.
- Real-Time Fraud v0.1.0 · CI — Replay protection is single-instance.
- Hyperion Architecture — Private runtime v0.4.0 deployed and health-validated on existing Hetzner; public Cloudflare route intentionally not configured.
- OSS Sentinel v0.2.0 · CI — Historical scores removed pending regeneration.
- My Orlando v0.3.0 · CI — Grounding is evidence gating, not an independent truth guarantee.
- Winter Garden Legal RAG v0.1.0 · CI — Synthetic CC0 fixture, not legal advice.
Google Cloud:
- Gen AI Leader
- Gen AI Agents: Transform Your Organization
- Transformer Models & BERT Model
- Attention Mechanism
- Build AI Agents with Enterprise Databases
IBM:
- Build RAG Applications: Get Started
- Develop Generative AI Applications: Get Started
- Generative AI: Introduction & Applications
Vanderbilt University:
- Model Context Protocol for Leaders: Generative AI Agents
- Agentic AI and AI Agents: A Primer for Leaders
- Prompt Engineering for ChatGPT
University of Colorado Boulder:
- Modern AI Models for Vision and Multimodal Understanding
Duke University:
- Python Essentials for MLOps
Whizlabs:
- Azure ML: Deploying, Managing & Experimenting with Models
Bachelor of Computer Science. Universidade Virtual do Estado de São Paulo (UNIVESP), 2018
Bachelor of Advertising and Marketing. Pontifícia Universidade Católica de São Paulo (PUC-SP), 2014
Portuguese (Native) • English (Fluent) • Spanish (Advanced) • French (Advanced)
Location: Winter Garden, Florida | Open to relocation
Email: cesardonahill3@gmail.com
LinkedIn: cesar-augusto
Philosophy: I focus on clarity, reproducibility, and documented limits in AI/ML systems.



