GE VERNOVA · EFFECTUAL / AWS PROSERVE
AI Architect & Senior Engineer
Architecture
The Problem
GE Vernova's global industrial Asset Performance Management platform contained decades of engineering knowledge locked in PDFs, CAD drawings, and unstructured documents. Extracting and operationalizing that data at scale required a new AI-native approach.
What I Built
Integrated Claude Vision via AWS Bedrock to extract structured data from complex engineering drawings and maintenance documents
Built Python/Flask microservices to transform extracted data into normalized records for the APM platform
Designed the end-to-end pipeline architecture: document ingestion → AI extraction → validation → platform sync
Collaborated with GE Vernova engineers to define schemas, edge cases, and acceptance criteria
Outcomes
Unlocked document intelligence at enterprise scale across GE Vernova's global APM platform
Reduced manual data entry time dramatically by automating extraction from engineering documents
Established a reusable AI pipeline pattern for future document-intelligence use cases
At a glance
Role
AI Architect & Senior Engineer
Stack
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