Applied AI workflow engineer building internal tools, automation systems, and human-reviewed AI prototypes.

I work across product engineering, embedded software, validation, demo software, and customer-facing technical support. My current focus is applying that systems mindset to practical AI workflows: turning messy operational requests into structured, reviewable, and testable outputs.

• AI workflow automation
• Internal tools and operational systems
• Human-in-the-loop AI prototypes
• Structured outputs and evaluation criteria
• Responsible AI boundaries
• Python-based demos and automation workflows

A sanitised public demo for turning unstructured civic or operational requests into structured, reviewable workflow outputs.

It demonstrates:

• request classification
• structured field extraction
• mock context retrieval
• risk assessment
• suggested next actions
• human review boundaries
• evaluation planning
• responsible AI constraints

Repository: https://github.com/MagnoCarlos/ai-civic-workflow-sandbox

My background includes:

• embedded firmware development
• product validation across EVT/DVT/PVT stages
• test strategy and metric definition
• software prototyping
• technical demos
• customer and sales engineering support
• platform benchmarking
• signal processing and computer vision foundations

• Python
• C/C++
• GitHub
• Qt Creator
• MATLAB/Simulink
• OpenCV
• Firebase
• n8n
• Google Workspace automation
• LLM workflow design

• MagnoCarlos — my professional engineering profile
• ai-civic-workflow-sandbox — applied AI workflow proof asset
• Ndala Builds — public AI automation demo lab

LinkedIn: https://www.linkedin.com/in/magnocarlos/