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cesaremcasa/README.md

Cesar Augusto

AI/ML Systems Engineer. I build, test, and operate AI/ML systems with reproducible releases and documented limitations.


⚡ Tech Stack

Python FastAPI TypeScript RAG CI/CD Structured logging


Engineering Focus

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

Selected Systems


Certifications

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

Education

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


Languages

Portuguese (Native) • English (Fluent) • Spanish (Advanced) • French (Advanced)


Connect

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.

Pinned Loading

  1. Real-Time-Fraud-Detection-with-Deep-Learning Real-Time-Fraud-Detection-with-Deep-Learning Public

    Streaming anomaly detection on taxi trip data. FastAPI producer, Redpanda, and a GPU worker running a trained autoencoder. Unsupervised, with the decision threshold derived from validation error.

    Python 1

  2. delta-one-architecture delta-one-architecture Public

    Delta One. A voice first Android environment built around intention, on AOSP. Architecture and design record. Models propose, deterministic code authorizes. Part of Mycellium Lab.

  3. hyperion-architecture hyperion-architecture Public

    Compound urban flood nowcasting for the next six hours. Method and architecture record. Calibration over accuracy, leakage inspection on every run. Part of Mycellium Lab.

  4. manager-architecture manager-architecture Public

    Operations intelligence for independent restaurants. Architecture and design record. Code calculates, the model narrates. Part of Mycellium Lab.

  5. Atlas.WM Atlas.WM Public

    Structured world model with physics validated predictions. Architecture validated on continuous dynamics (v2.0). Built for robotics applications.

    Python 1