Technology & AI / Product and professional work
Turn operational information into useful decisions.
My technology work connects data engineering, operational analytics and manufacturing product development, grounded in the problems people need to solve.
Relevant experience
- AutoOps AI · Personal product development
- Founder and product engineer. Manufacturing context, source-linked reasoning and structured quality workflows; 35 discovery interviews and 20 pilot users. Pilot participation is not a quantified customer outcome.
- Epic Systems · Professional data engineering
- Built SQL/ETL pipelines processing 500K+ records, with a reported 30% data-quality improvement supporting operational dashboards. No healthcare records are published.
- Disney · Professional operational analytics
- Python predictive and optimization models supported a reported 25% reduction in cruise-port wait times. Transportation and material-flow redesign delivered an 82% cost reduction ($120K annual). These are resume-level results; no underlying operational dataset is published.
AutoOps AI / Architecture summary
Documents & records
Uploaded context
Uploaded context
SAP connector
Primarily mock-tested
Primarily mock-tested
- 01Structured manufacturing context
- 02Retrieval & AI reasoning
- 03Quality workflows + cited output
Personal product development
AutoOps AI: product, architecture and maturity
Beta workflows, mock-tested SAP integration, pilot participation and planned capabilities are distinguished within the case study. Production deployment status is not independently verified.
Professional analytical work
Data applied to production performance
The manufacturing case shows how data quality, segmentation and process control change operational decisions.
Try the engineering lab
Put an engineering decision to the test.
Explore layout, work design, quality, manufacturing ML and production flow in one fictional factory. All demonstrations are synthetic and separate from professional work.