Based in Toronto, Canada • System online
A hands-on data and AI leader with 10 years of experience moving from architecture to adoption — building the platforms, teams, and operating systems that make transformation stick.
Enterprise work, framed as problems solved—not a wall of technologies.
Led the assessment, architecture, and change program for an enterprise migration to Microsoft Fabric across five core business units.
Reduced Azure SQL and compute costs by 10–15% through platform consolidation, capacity management, and performance tuning.
A governed documentation layer for both people and AI agents—built to turn changing Office files, PDFs, repositories, datasets, and notes into a source-backed knowledge workspace.
A career built by moving outward: from technical depth to organizational leverage.
Leading an eight-person cross-functional team across enterprise data infrastructure, Fabric transformation, applied AI, governance, and platform economics.
Progressed from BI developer to senior manager; led analytics teams and delivered forecasting, automation, enterprise SaaS integration, and decision systems. Recipient of the LG Excellence and LG Ovation awards.
Led tutorials, exam reviews, and targeted mathematical support — an early foundation for translating complex systems clearly.
My AI work centers on coordination, reusable expertise, governed evidence, and the delivery system around the model.
I design coordinated agent teams as operational systems: explicit roles, bounded authority, observable handoffs, and independent review contexts.
The engineering focus is on separation of duties, least-privilege tools, approval currency, bounded retry loops, failure contingencies, and evidence that survives beyond a single session.
I have developed 10+ production agent skills that turn expert data and cloud workflows into reusable, discoverable capabilities.
Examples include database assessment, data-lineage and dependency scanning, and Azure cost intelligence—with explicit trigger rules, progressive context, validation gates, and safe fallbacks.
I build agents that meet people inside real enterprise workflows, grounded in governed evidence rather than disconnected model output.
Work includes RAG knowledge systems, Azure AI Foundry and AI Search, plus LLM-assisted catalog, relationship, and transformation-logic generation for data teams and business users.
I treat evaluation, observability, cost, and deployment discipline as part of the AI architecture—not an afterthought.
That means CI/CD/CT patterns, measurable acceptance gates, review loops, capacity economics, and a safe path from experiment to governed production.
Optimization, strategic behavior, learning systems, and business application are not separate chapters—they are the intellectual spine of my work.
Mathematics gave me a language for constraints, trade-offs, networks, and strategic behavior. The MMAI program added modern learning systems and the management discipline to deploy them responsibly. That combination still shapes how I engineer AI today.
Mathematical Optimization • Operations Research • Joint Honours Statistics
Waterloo taught me to model decisions before optimizing systems—and to stay rigorous when the objective, constraints, or other players change.
Queen’s University
The MMAI experience connected model design to adoption, economics, and executive decisions—the bridge between technical possibility and business value.
Public work in motion—because credibility should leave a trail.
A local-first, governed documentation layer that connects changing sources to durable knowledge for people and AI agents.
Practical writing on enterprise AI, Fabric economics, knowledge systems, and the craft of transformation.
Teams with document-heavy operations, strong provenance needs, and an appetite for disciplined AI adoption.
Recruiting • Partnerships • Design partners • Good questions