AI Experience Engineer

Accountabilities:

• Own the full-stack design and engineering quality of AI pilots and prototypes, taking concepts from briefs or wireframes through backend services, APIs, data integrations, and polished production-quality interfaces.
• Build intuitive, accessible, and visually consistent experiences that make complex AI capabilities immediately understandable to technical and non-technical stakeholders.
• Design and implement human-on-the-loop interaction patterns that allow users to inspect agent traces, review outputs, adjust parameters, identify anomalies, and intervene when appropriate without becoming bottlenecks in every AI workflow.
• Develop and maintain a shared design system and component library featuring AI-specific patterns such as streaming outputs, agent status indicators, progress states, trace inspection views, confidence surfaces, and human-review interfaces.
• Establish UX and interaction standards for AI assistants and copilots, including contextual assistance, real-time knowledge retrieval, AI-generated content, and role-specific intelligence dashboards.
• Conduct lightweight user research and usability testing with internal stakeholders and business partners, using feedback early enough to influence product direction and functionality.
• Collaborate with AI engineering partners to create evaluation interfaces and observability dashboards that make model behavior, agent traces, and system performance understandable to human reviewers.
• Champion WCAG accessibility standards, frontend performance, and high-quality engineering practices across innovation deliverables.
• Apply established brand and design standards to customer-facing and externally visible experiences.
• Contribute to frontend technology evaluations and emerging technology assessments, helping define technical direction for AI interaction patterns.
• Create and document reusable architectural and interaction patterns so successful AI experiences can be adopted by broader engineering teams and carried into production.
• Establish strong foundations early, including auditing existing design systems, shipping initial agent interaction components, building observability interfaces, and documenting design-system foundations for production teams.

Requirements:

• Strong full-stack engineering capabilities with significant frontend depth, including the ability to build backend services, integrate data sources, and deliver polished interfaces within the same development cycle.
• Demonstrated production experience building AI-powered systems, particularly applications involving streaming output, agent interactions, human-review workflows, trace inspection, or similar AI interfaces.
• Understanding of the non-deterministic nature of LLM-powered systems and the importance of designing for continuous inspection, observability, and human intervention.
• Strong experience with React or an equivalent modern frontend framework and contemporary design-system practices.
• Ability to move fluidly between UX design and implementation, including working with tools such as Figma and translating interaction concepts directly into production code.
• Experience designing interfaces for complex technical information while maintaining clarity and usability for non-technical audiences.
• Strong product intuition and a user-centered mindset, with the ability to determine what users actually need from an AI system before deciding how information should be presented.
• Experience with agent observability platforms such as Langfuse, LangSmith, Phoenix, Arize, or equivalent solutions is highly valued.
• Knowledge of data visualization, WCAG accessibility standards, and performance engineering is advantageous.
• Familiarity with LLM evaluation frameworks, eval harnesses, or LLM-as-judge approaches is a plus.
• Strong communication and collaboration skills, with the ability to work effectively across engineering, product, design, and business teams.
• Comfortable operating in an innovation environment where requirements evolve quickly and prototypes must balance experimentation with production-quality standards.
• Ability to take ownership of highly visible work, challenge assumptions constructively, and maintain a high bar for quality and usability.