I architect enterprise AI strategies that translate emerging GenAI capability into production-grade, governed systems — built on 15+ years of enterprise architecture experience across data, integration, and cloud platforms.
Gen AI Solution ArchitectureEnterprise AI StrategyData Platform for AIOpen to Permanent or ContractGreater Toronto–Waterloo · Remote-first, Canada-wide
What I Do
I design GenAI-enabled enterprise systems — RAG platforms, agent frameworks, and AI-native data architectures — that integrate with existing infrastructure rather than replacing it.
Who I Work With
CTOs, CDOs, and Heads of Engineering at large & medium enterprises navigating the gap between AI experimentation and production deployment at scale.
My Approach
Strategy first, technology second. I define the architecture decision, the trade-offs, and the governance model before a line of code is written.
Background
15+ years in enterprise architecture, data platforms, and systems integration — most recently applying that foundation to GenAI delivery at enterprise scale.
Focus Areas
Four interconnected disciplines that define where I create the most value for organisations moving from AI experimentation toward production-grade deployment.
01 — ARCHITECTURE
Enterprise AI Architecture
Designing GenAI-enabled systems that integrate LLM-powered workflows, RAG implementations, and AI-native features into existing enterprise infrastructure — with security, governance, and scalability built in from day one.
02 — DATA
Data Platform for AI
Building modern data foundations — vector databases, semantic search layers, and real-time pipelines — specifically designed to power LLMs and RAG systems without dismantling the existing data estate.
03 — STRATEGY
GenAI Strategy & Governance
Developing practical GenAI adoption frameworks that go beyond POCs — covering model selection, prompt engineering standards, AI security patterns, and responsible AI governance for production deployment.
04 — TRANSFORMATION
AI Transformation Leadership
Bridging the gap between AI vendors, technical teams, and business leadership — delivering AI solutions with clear ROI, proper governance, and alignment to strategic objectives rather than isolated prototypes.
How I Work
Successful AI adoption isn't about chasing every new model — it's about knowing where AI creates genuine business leverage and building the architecture to sustain it.
01
Architecture before implementation
Every engagement starts with the architecture decision — component selection, integration patterns, data flows, and trade-offs — documented before implementation begins. This prevents the drift from prototype to production debt.
ADRsC4 DiagramsTrade-off Analysis
02
Data foundations that serve AI systems
AI systems are only as good as the data that feeds them. I design data architectures alongside AI architectures — ensuring clean, contextual, governed data reaches LLMs and RAG pipelines without disrupting operational systems.
GenAI solutions that sit beside existing systems don't deliver ROI — they create parallel overhead. I architect integrations that embed AI capability directly into the workflows, APIs, and platforms the business already uses.
LangChain / LangGraphRAG PipelinesFastAPI / REST
04
Governance and clarity across stakeholders
C-suite, technical teams, and AI vendors speak different languages. I create the governance frameworks, roadmaps, and communication artefacts that align all three — turning ambiguity into a clear deployment plan with defined accountability.
Architectural convictions that shape every engagement
AI adoption fails at the architecture layer, not the model layer. Most enterprise AI failures trace back to integration gaps, data quality problems, and missing governance — not to the LLM being insufficiently capable. The model is rarely the bottleneck.
Vendor neutrality is a strategic asset, not a preference. Every architectural decision that creates lock-in at the LLM or infrastructure layer increases long-term cost and reduces optionality. Modular, abstracted architectures age better.
Governance built in beats governance bolted on. Security, RBAC, data lineage, and responsible AI controls are cheapest when designed into the architecture at the start. Retrofitting them after deployment is expensive and incomplete.
A working prototype and a production architecture are different artefacts. The path from POC to production requires explicit decisions about scalability, observability, cost control, and failure modes — decisions that most POCs deliberately defer and then forget to revisit.
Technology Signals
The stack I design with and deploy against — weighted toward the patterns that appear in enterprise production, not just tutorials.
LLM Orchestration
LangChainLangGraphLlamaIndexSemantic KernelOpenAI Assistants API