I lead complex engineering programs from concept through validation and manufacturing readiness—connecting technical depth with structured execution.
My background spans medical devices, advanced manufacturing, laser and optical systems, engineering analytics, and digital-thread / PLM environments. I build practical systems that help teams turn fragmented technical work into traceable decisions, clear priorities, and measurable progress.
🌐 Utmost Connect · Engineering & technical product-development consulting
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Navier–Stokes AI — CFD Results Dashboard
Interactive CFD verification with geometry, mesh, pressure and velocity profiles, solver gates, a technical report, and AI prediction evidence. Public reproducible CFD case study includes a runnable annulus benchmark and saved-field postprocessing. Numerical model snapshot; external validation pending. Full research source remains private. -
ClearPath QMS — Traceability & Audit-Readiness
Synthetic-data demonstration of evidence gaps, traceability coverage, risk-control verification, and regulated product-development workflow. -
ClearPath — AI-Guided Medical Device Project Management
Public-safe case study of an AI Coach, lifecycle-based project workflow, auditable activity history, and human-governed agent design. -
AmirOS — Engineering Intelligence
Synthetic-data executive dashboard for engineering context, evidence, deterministic readiness assessment, and human-governed review. -
Orthodontic Design Performance Analytics
Synthetic-data dashboard translating orthodontic measurement data into arch/tooth-pair variation analysis and cross-functional R&D, Quality, and Manufacturing review priorities.
- Product & R&D Leadership — concept development, technical strategy, cross-functional execution, and program delivery
- Regulated Product Development — design controls, risk management, V&V, quality systems, and manufacturing readiness
- Laser, Optical & Advanced Manufacturing Systems — precision process development, optical metrology, DFM/DFA, and automation
- Engineering Intelligence — Python analytics, decision support, technical workflows, and reusable engineering knowledge
- PLM & Digital Thread — traceability, requirements, configuration, and lifecycle data
| Area | What it demonstrates |
|---|---|
| Engineering Intelligence | Turning engineering context, decisions, and next actions into an organized execution system |
| Quality & Traceability | Practical workflows for risk, verification, traceability, and design-control readiness |
| Engineering Analytics | Using synthetic and de-identified data to identify product and manufacturing opportunities |
| Technical Automation | Reusable tools that reduce friction in engineering analysis, documentation, and execution |
Confidentiality note: Public projects use synthetic, public, or de-identified material only. No confidential employer, client, clinical, or proprietary data is shared.
Ph.D. in Biomedical Engineering with experience across clinical translation, medical-device R&D, high-precision manufacturing, data-driven product development, and technical program leadership.
I welcome conversations about engineering leadership, technical product development, regulated systems, and consulting collaborations.