I lead design for AI and data-intensive products.
Fifteen years of decisions across five industries. Frameworks public, products NDA, outcomes measured.
Data-literate at the model layer. I shape what gets measured. I ship the front-end. · Founding · Staff IC · Director-level · Available · 4-weeks notice
Selected Work
Six projects across EdTech, Telecom, AdTech, FinTech, organizational design, and VC tooling. Featured first: the two I can show. The four below are NDA — happy to walk through under mutual NDA on a call.
PTC University
Multi-platform learning consolidation across 11 languages. The redesign wasn't the hard part — the political case for killing seven products was.
O2 Priority Billing
Telecom bills were correct and opaque. Three design decisions cut "explain my bill" tickets 40%. Decisions and component spec shown; client paraphrased.
Four more · NDA · Walk-through on request
Programmatic Advertising
Confidence-first design for media buyers who wouldn't act on the algorithm's calls.
AI-Assisted Due Diligence
Refused to ship the model without an "explain this score" surface. PE analysts started using it.
OrgOS · Transparent Org Tooling
Eight modules letting a 120-person team self-organise. Zero managers; transparency as coordination.
Technical Due Diligence Platform
AI-extracted technical signals + partner-grade trust. Cycles compressed from 3 weeks to 4 days.
Patterns
AI Design Patterns Library
Patterns I've shipped into production for AI products across five industries. Each one carries the tradeoff I learned the hard way — what I tried, what broke, what I changed.
Confidence Score Patterns
5 core patterns for displaying AI confidence: numeric, color-coded, language-based, and composite approaches.
AI Failure States
How to communicate uncertainty, errors, and limits transparently when AI systems can't deliver.
ML Explainability
Patterns for making ML decision-making visible to non-technical users without overwhelming them.
Human-in-Loop Patterns
Interface patterns for keeping humans in control when AI handles high-stakes recommendations.
How I Lead
I'm the design lead who reads your model evals, sits in on your customer calls, and opens PRs for the components I own. By the end of week one, I've written the design diagnosis your team didn't know they needed.
What you're hiring me to own
- The trust layer
- The product surface where users decide whether to act on the model — or override it.
- The design language
- The component system and patterns the next designer inherits — written down, not in my head.
- The ML/UX contract
- The explicit agreement between the ML team and the user about what the system can and can't do.
How I partner with each function
- With engineering
- I pair on eval design before I touch the UI. I open PRs for the CSS I own. When eng and I disagree about feasibility, I default to the cheaper experiment first.
- With product / founder
- I push back on the roadmap when the data says we're wrong. I commit to outcomes, not deliverables. I write the design doc; you write the spec.
- With customers
- I sit in on five calls in week one and one a week after. I read the support tickets myself. I won't ship an AI feature until I've watched a user fail with it.
When I've been wrong
I argued the recommendation card should show three ranked options. Engineering pushed for one — the top-ranked, no alternatives. I lost the argument. In the next quarter's A/B test, the one-option version converted 2.3× better — three options made users freeze. I changed how I design every recommendation surface after.
What I won't do
- I won't be the only designer past 40 people. Past that, design becomes an org problem and I want a teammate, not martyrdom.
- I won't ship an AI feature without a designed failure state. Non-negotiable. If your team disagrees, we're not a fit.
- I won't run design ops in parallel with shipping product. Pick one. Doing both badly is the most common senior-designer trap.
Writing
The Agentic MVP: Why Your Next Launch Will Be Lovable, Not Just Viable
Every founder knows the pit in their stomach on Launch Day. How the rise of agentic systems is rewriting what "minimum viable" means — and why lovability is now the bar.
The AI Fight Club: Weaponizing Claude and Gemini for Bulletproof Products
Pitting AI systems against each other to strengthen product robustness. A practical method for stress-testing your AI features before users do it for you.
The New Renaissance: How AI is Transforming Us from Software Operators to Digital Artisans
Ushering in a new era of digital entrepreneurship. How AI is changing what it means to build, and what designers must understand about the tools reshaping the industry.
Contact
Hire me to lead design.
Founding · Staff IC · Director-level · Available · 4-weeks notice · Fully remote (GMT+5:30) · 4–5 hr daily overlap with US East Coast
Book a 30-min intro callOr pick a different shape:
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Founding / Staff role Primary · Full-time · equity5–40 people, post-PMF or 2–4 quarters out. Designer #1 or #2. Direct line to founders, ships code, owns trust UX from day one. 30 min →
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0-to-1 partnership 12–24 weeks · 25–40 hrs/wkNew AI product, prototype to launch. I take a seat on your team. Output: shipped product, not a Figma file. 45 min →
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Trust-layer audit 2-week sprint · fixed feeLive but adoption is flat. I diagnose, prescribe, written brief in 10 working days. 15 min →
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Advisory 4–8 hrs/month · ongoingYou have a designer, you want a sparring partner. Quarterly strategy reviews, ML UX audits, hiring help. 15 min →
Async option: Send me a link to your AI product — I'll record a 5-min Loom with three actionable improvements within 48 hours.
What it's like working with me