About Me
Committed to advancing how enterprises think about AI architecture, reliability, and strategic adoption. Recognized for defining production patterns that others follow.
Background
My path from Aeronautical Engineering through industrial management to AI systems leadership has positioned me to see problems holistically—connecting technical excellence with strategic business outcomes. This background shapes how I architect systems and define patterns that work at scale.
Most of my work is in enterprise AI: chatbots that connect to real knowledge bases with proper permissions, agents that orchestrate tools safely, RAG pipelines that don't hallucinate because they're grounded in source documents. The kind of systems that have to be secure, auditable, and reliable—because they're running in production for teams that depend on them.
I focus on the unglamorous parts that make AI actually work: how do you chunk documents so retrieval doesn't suck? How do you handle permissions when users query across multiple systems? How do you build eval harnesses so you know if your changes make things better or worse?
I advance practical approaches to AI architecture that others adopt because they solve real problems. I define patterns, not chase trends.
Education
University of Brighton, UK
BEng (Hons), Aeronautical Engineering
Engineering fundamentals: systems design, optimization, failure analysis. Learned to think about how things break under real-world constraints.
University of Texas, USA
MS, Industrial Management
Bridging engineering and operations: how teams adopt technology, how to measure impact, how to build systems that scale beyond prototypes.
How I Work
Design for Production-Grade Systems from Concept
Prototypes are fine for demos, but I architect for what happens after the demo: permissions, error handling, audit logs, eval harnesses. The systems I define need to keep working when they scale and when things go wrong.
Measure everything that matters
If you can't measure it, you can't improve it. I establish eval frameworks, define metrics, and run experiments before making changes. Vibes-based optimization doesn't work at scale.
Security and auditability are not optional
Enterprise AI needs guardrails: least-privilege access, permission passthrough, full audit trails. These aren't features you bolt on later—they're architectural decisions from the start.
Simplicity over cleverness
The simplest solution that works is usually the right one. Add complexity only when you have evidence it's needed—not because it might be useful someday.
Technical Stack
AI & LLM Systems
Backend & APIs
Frontend & Web
Cloud & Infrastructure
Approach
Work with Me
I'm available for strategic consulting, technical advisory roles, thought partnership, and architecture definition. If you're defining your AI strategy or need architectural guidance, let's talk.