Asset Integrity Engineer
RBI Decision Systems • Engineering Data Architecture • Physics-Constrained ML • Secure Automation
I design and implement decision-support systems for asset integrity and risk-based inspection (RBI) programs.
My work operates at the boundary between engineering judgment, structured data systems, and applied machine learning. The objective is not algorithmic novelty — the objective is defensible operational decisions.
Systems are built to ensure:
- Explicit assumptions
- Transparent transformation logic
- Inspectable data lineage
- Bounded model behavior
- Visible failure modes
flowchart TD
A[Physical Reality] --> B[Structured Engineering Data]
B --> C[Explicit Domain Logic]
C --> D[Physics / Risk Framing]
D --> E[Validation Harness]
E --> F[Operational Decision Support]
Engineering judgment is preserved — not replaced.
Automation reduces ambiguity, not accountability.
Architectural priorities:
- Schemas aligned with physical meaning
- Validation at ingestion
- Deterministic transformations
- Version-controlled logic
- Full audit trail
Data quality is treated as an engineering risk variable.
Primary language: Python, HTML, Javascript
Design emphasis: Reproducibility • Auditability • Determinism
If an assumption is not written, it will fail silently.
If data lineage is not explicit, the decision is not defensible.
If a model cannot define its boundary conditions,
it is not operational.
feliper@infinitygrowth.ca
felipe@olivainternationaltech.com
https://www.linkedin.com/in/felipe-rocha-7a944b133/
Open to technical discussions involving:
- Asset integrity digitalization
- RBI architecture
- Physics-informed modeling
- Engineering-grade automation
- Secure industrial data systems