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AAA Club Alliance

Automotive membership and travel services

Lead Design Technologist - Design Systems

Frontend EngineeringPosted on Nov 5
Full-timeRemote in USA$152k - $202.6k

About the Job

Requirements

  • A bachelor's degree or equivalent practical experience.
  • At least 8 years of progressive experience in design systems, front-end engineering, design technology, or experience architecture, including technical ownership or stewardship of a design system implemented in code.
  • Strong hands-on front-end engineering experience with a modern component framework, including component structure, composition, and API design.
  • Experience implementing and governing design tokens, component libraries, and the technical expression of reusable UX patterns at enterprise scale.
  • Practical experience with design-to-code workflows and the tools that connect design assets to production components.
  • Working fluency with AI-assisted development tools and a practical understanding of how AI models use context, skills, and tool definitions.
  • Ability to define the structure and technical direction of a shared system and partner effectively with Engineering and Architecture to bring it to life, rather than needing to build every part personally.
  • Strong written communication skills and the ability to create documentation and standards that teams can understand and use.
  • Demonstrated ability to influence across design and engineering teams without formal authority.
  • Comfort working through ambiguity, including in areas where the field and standards are still evolving.
  • Ability to translate between design and engineering audiences, turning design intent into implementable system structures without losing important details in either direction.
  • Applicants must have authorization to work indefinitely in the United States and must not require immigration support now or in the future.

Responsibilities

  • Define and maintain the technical structure of the enterprise design system, including the organization and relationships of components, tokens, patterns, shared packages, reusable capabilities, and reference implementations.
  • Partner with UX Design to translate approved patterns, templates, and design decisions into scalable code structures, including component APIs, design tokens, shared packages, and implementation standards.
  • Maintain the connection between design tooling and code so approved UX definitions can be implemented accurately and consistently in production.
  • Define how design-system assets are structured, composed, governed, and versioned in code for consistent reuse across products and applications.
  • Partner with UX and platform teams to determine how design standards and technical assets translate into third-party platforms such as Pega and Guidewire, defining direct reuse, platform-specific implementation, documented extensions, and exceptions.
  • Define processes for contributing, updating, deprecating, and versioning shared system assets without disrupting dependent teams.
  • Set criteria for what should become a shared system asset and what should remain specific to a product, platform, or solution.
  • Define and maintain AI design-system foundations, including shared system prompts, voice and tone standards, refusal and escalation behavior, and safety policies.
  • Define reusable AI capabilities, including skills, tool and function definitions, Model Context Protocol servers, and approved retrieval and context sources.
  • Define standards for how AI agents exchange context, call tools and services, return structured outputs, handle permissions, and interact with systems and other AI agents.
  • Create reusable prompt templates and reasoning and orchestration patterns for handoffs, delegation, human review, and common workflows.
  • Create reference implementations, including example AI agents and starter structures teams can adapt.
  • Maintain shared evaluation suites to assess whether AI behavior remains within expected quality and performance ranges as the system changes.
  • Provide tracing and instrumentation to make AI-agent actions, tool and context use, and problems observable.
  • Make system assets discoverable and understandable to people and AI agents, with descriptions of their function and appropriate use.
  • Define guardrails and governance for approved AI capability use in partnership with AI governance and the AI Council.
  • Maintain a coherent system across internal and customer-facing experiences for both people and AI agents.
  • Put design-to-code tooling and technical connections in place before development, including component connection tooling, design and component Model Context Protocol servers, shared packages, and token distribution.
  • Decide the appropriate degree of integration between approved design definitions and code for each component set.
  • Stay engaged during delivery to trace issues to design definitions, base component code, connection tooling, shared packages, tokens, or platform constraints.
  • Help UX and engineering teams understand how design decisions flow into the technical system and close gaps that lead teams to bypass shared assets or create avoidable rework.
  • Make the system easier to adopt, including reducing the time for a new team to move from setup to first successful use.
  • Define the structure and technical requirements for shared system assets, then partner with Engineering to build and operate them and Architecture to determine integration and placement in the enterprise technology ecosystem; the role does not own the full production code lifecycle or enterprise architecture decisions.
  • Work with Engineering and Architecture on feasibility, dependencies, sequencing, and estimates for system-level work.
  • Clarify ownership across shared design-system assets, enterprise architecture, and production application code, and communicate those responsibilities to partner teams.
  • Build prototypes and proofs of concept to test structural decisions or reduce risk when useful.
  • Stay connected through delivery to ensure implementation reflects approved UX intent, design-system technical standards, and agreed enterprise architecture decisions.
  • Work across stewarded, consultative, and self-service engagement models according to the complexity and needs of the work.
  • Build the system, documentation, and tooling so more teams can use it independently over time, treating the shift toward self-service as a key measure of success.
  • Create documentation, examples, and enablement materials that teams can use without ongoing support from the central team.
  • Track adoption, reuse, and duplication across teams and use the data to improve the system.
  • Support engineers and designers with questions about system use and implement feedback.
  • Partner with UX, Architecture, CX and business partners, Engineering, platform teams, and AI governance to connect stakeholders and develop shared system assets.
  • Partner with the Workflow and Systems Design practice to translate future-state workflows into reusable technical foundations and identify recurring capabilities for reuse.
  • Work with owners of reusable design or workflow patterns emerging from project work to determine which should become shared technical system assets.
  • Communicate system constraints, structural decisions, and the roadmap to design, engineering, and architecture audiences.
  • Contribute repeatable technical methods, standards, and implementation templates to mature the Enterprise Systems Design practice.
  • Contribute to onboarding and domain materials that help new practitioners and partner teams ramp up faster.
  • Stay current on design systems, especially systems designed for AI and AI-agent use, and bring relevant practices back to the team.

Desired Qualifications

  • A degree in Computer Science, Human-Computer Interaction, Design Technology, or a related field.
  • Relevant training or certification in front-end engineering, design systems, accessibility, or AI-related disciplines.
  • Experience building or governing AI- and agent-facing capabilities such as skills, tool definitions, Model Context Protocol servers, or governed context sources.
  • Experience with evaluation and observability practices for AI systems.
  • Experience in insurance, financial services, or another regulated industry.
  • Experience supporting both employee-facing and customer-facing products or experiences.
  • Experience adapting enterprise design-system standards to third-party or vendor-owned platforms where the underlying component system cannot be fully controlled.
  • Experience creating adoption models, contribution models, or federated governance for a shared system.
  • Experience coaching engineers or designers on adopting and using shared systems.
  • Familiarity with accessibility standards and how they are expressed and enforced through component systems and code.

About the Company

AAA Club Alliance

Automotive membership and travel services

AAA Club Alliance is a membership organization providing roadside assistance, automotive services, insurance, travel planning, financial products, and member discounts across its operating territory. It serves drivers, travelers, households, and businesses through digital channels, contact centers, local stores, car care locations, and affiliated service operations. The organization brings together several regional AAA clubs and trade names under a shared operating structure. Teams work across roadside service, automotive repair, insurance, travel, membership support, retail operations, financial services, technology, marketing, and corporate functions.

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