Lead AI Design Engineer

Our client is seeking a Senior Design Technology Leader to architect and scale an AI-native design and development ecosystem.

This is not a traditional Head of Design role, nor a sandbox for isolated AI experimentation. The core mission is structural: re-engineering the end-to-end product lifecycle so multi-disciplinary teams can move seamlessly from concept to production-ready software at unprecedented speed.

The client has already moved past initial tool testing. They now require a systemic operator who has successfully built, deployed, and scaled production-grade AI workflows, design systems, and cross-functional operating models in real-world environments.

Key Responsibilities

Systemic Workflow Architecture: Architect and roll out end-to-end, AI-first workflows that unify product design, design systems, and frontend engineering.

Accelerated Time-to-Production: Compress cycles from initial ideation to validated prototypes, component integration, and production code.

Bridge Design & Code: Connect Figma/design artifacts directly to production codebases using token-based architecture and component libraries.

Tooling Evaluation & Integration: Assess emerging AI technologies through a pragmatic lens, filtering hype to integrate tools with high production value.

Organizational Enablement: Drive cross-functional change management, establishing guardrails and coaching designers and engineers on scalable AI workflows.

Metrics & Optimization: Track and optimize core KPIs across delivery velocity, iteration friction, and code maintainability.

What We’re Looking For

Proven Production Track Record: Demonstrated experience embedding AI-native systems into active design and software engineering pipelines.

Hybrid Technical Depth: Strong command of design systems, modern frontend architecture, and the mechanics of design-to-code translation.

Product & Systems Thinking: Ability to look beyond individual tools and optimize the holistic software delivery lifecycle.

Pragmatic AI Literacy: Clear distinction between flashy tech demos and production-ready AI capabilities.

Change Leadership: Hands-on experience driving adoption across design, product, and engineering organizations.

Communication: Business-fluent written and spoken English.

Key Evaluation Criteria (What Matters Most)

The client values concrete proof of deployment over theoretical knowledge. Be prepared to share case studies demonstrating:

Before/After Architecture: Baseline workflow vs. the AI-native state you engineered.

Infrastructure Built: Specific AI models, internal agentic tools, plugins, or design tokens introduced.

Production Integration: How design artifacts dynamically connected to real codebases.

Adoption & Scale: Organization-wide adoption rates among engineers and designers.

Measurable ROI: Quantifiable impact on iteration cycles and time-to-market.

Nice to Have

Background as a Design Engineer, Design Technologist, or Product Engineer.

Hands-on experience building custom AI tools, agents, or plugins for internal dev/design teams.

Mastery of modern AI coding assistants, generative UI tools, and token-based design systems.

What Success Looks Like

Success in this role means fundamentally altering how products are shipped. You will eliminate manual handoffs, establish reliable design-to-code connectivity, and establish a repeatable, high-velocity operational model that turns AI into a core competitive advantage.