Michelle Bajurny

Michelle Bajurny

Principal product designer · systems, AI & leverage

Canada, Remote (PST)

I design the systems underneath complex products - and increasingly, the AI-enabled workflows that let teams build them faster. Over the last decade I've worked across enterprise marketplaces, regulated financial services, and AI-native health, usually on the problems that don't fit neatly inside a single screen or a single team.

From Cisco Meraki to AI product design

I spent years as a lead designer inside Cisco's Meraki organization, working on the App Marketplace - an enterprise surface used by more than a million admins a month. At that scale I learned the lesson that still shapes how I work: the quality of an experience is decided in the seams. The individual screens were the easy part. The hard, durable work was the shared event taxonomy, the design-system governance across squads, and the instrumentation that let design, product, and growth argue from the same data instead of from opinion.

That pulled me toward the foundations rather than the surface - the standards, systems, and shared practices that raise the ceiling for everyone doing the work. When AI arrived as genuine design material, it fit the same instinct: another layer of leverage, if you treat it seriously.

Developing a point of view on AI-enabled work

I've been designing AI products for a few years now, and the same pattern keeps showing up: the model usually isn't the bottleneck. What separates AI products that get adopted from ones that don't is the surface around the model - whether a real operator can compose, test, and trust the thing inside their actual workflow. So I treat AI primitives as things to be designed: when the model should sound confident, when it should hedge, when it should defer to a human.

AI also changed how I work, not just what I design. Claude Code, Cursor, and MCP are part of my daily practice. I use them to prototype real interactions - async states, latency, fallback paths - before committing to polished mocks, and to turn repetitive design and research work into shared, reusable skills a whole team can draw on. My interest isn't AI enthusiasm for its own sake; it's grounded, practical leverage, with a clear view of where human judgment still has to win.

How I partner with product and engineering

I do my best work as a design partner to product and engineering leadership on the problems that span teams. I read React and Tailwind well enough to pair in the build, not just the handoff - leaving line-level PR comments, proposing component contracts, and catching early when the design and the data shape need to evolve together. I write to think, because the highest-leverage design decisions happen in problem framing, long before any pixels, when changes are still cheap and a document can align a whole org on direction.

What I'm focused on now

Systems, leverage, and team effectiveness. I'm most energized when the deliverable isn't a feature but a capability - a design system, a shared agent or skill, a research practice, a standard - something that outlasts the initiative that prompted it and makes the next ten projects better. That's the work I want more of.

Tools I work in

The stack behind the work here. AI is daily design material for me, not a demo - I use it to prototype real interactions and turn repetitive design and research work into reusable leverage.

AI & build
Claude / Claude CodeCursorCodexLovableMCP
Design
FigmaSpline

Experience

Download résumé (PDF) →