About

Principal Product Designer for Complex Systems

I partner with product leaders and executive teams to turn complex business challenges into clear product direction and trusted experiences across SaaS, FinTech, Healthcare, and AI-driven systems.

U.S.-based and available for remote roles nationwide, I've spent my career working where ambiguity meets scale, setting the vision, aligning teams, and carrying the work through to production.

Here is what you can rely on me for:

  • Own the outcome. I set direction, craft the experience, and follow it all the way to production. I know when something isn't right, and I can name the reason before any research says so.
  • Simplify the complex. I turn business goals, user needs, and complex data into intuitive products that reduce churn and get users onboard faster. I talk to users directly and let their contradictions reshape my thinking.
  • Build for the long term. I architect 0-to-1 visions and scalable design systems that cut development cycles, most recently reducing launch cycles from 3 months to 3 weeks. I optimise existing systems as much as I build new ones.
  • De-risk the unknown. I navigate multi-stakeholder environments and strict regulatory frameworks so you can innovate without breaking things. I've built where the brief was incomplete and the roadmap was a guess. I ship, learn, and iterate.
  • AI as infrastructure. I set the boundaries AI generates within, then validate the output. I use AI to compress concept-to-test cycles from weeks to days and design interfaces users actually trust, where consistency and reliability matter as much as innovation.

Available for: Staff, Lead and Principal Designer roles • Design consulting • Fractional Design Leadership

How I use AI in my design work

The shift I care about is not going faster on individual tasks. It's setting up the system so quality stays consistent across everything the team produces.

Research

  • I decide what I'm looking for before AI touches the data, so it sorts against my framework rather than inventing its own themes
  • It finds patterns across transcripts, surveys and analytics that I would otherwise miss

Ideation

  • I use AI to explore information architecture and flows, then pressure-test the options before committing
  • I have conversations with my design files to audit structure and generate variations

Prototyping

  • I build components and whole pages directly in code, skipping static mockups when I need something working
  • Components live in Storybook, so states and variants are documented as I build them

Design system

  • Tokens connect end to end, so the prototype, the spec and the code all reference the same values
  • Everything is versioned in Git, so design and engineering work from the same source
  • When starting from scratch, I use AI to generate and iterate on the system itself

Accessibility

  • I've built skills that check components against WCAG AA automatically, so accessibility is a structural default rather than a manual pass afterwards
  • Contrast, touch targets and focus order get caught as I work, not in review

Specification and handoff

  • I generate structured PRDs from prototypes covering states, acceptance criteria, edge cases and behaviour notes, so engineering builds from a clear source of truth

Where I'm careful

  • AI ignores established patterns and builds bespoke every time unless the system constrains it. That's the real reason tokens and documented components matter, not consistency for its own sake.
  • It also produces work that looks right and falls apart in the edge cases, empty states, error handling, focus order. I treat the output as a draft to interrogate, not a result.

Current stack: Codex · Claude Code · Figma · Storybook · Git

Let's Work Together

Interested in collaborating on your next project?