Senior AI Experience Platform Architect

Senior AI Experience Platform Architect

Remote

Full Time

What this role will own

•       Define and implement a clean boundary between reusable AI tools and the knowledge sources they consume, so information can change without rebuilding the tool and the same capabilities can be reused across departments.

•       Structure and curate trusted knowledge across user research, brand and design-system guidance, front-end components, accessibility requirements, and UX-writing standards. This involves taxonomy, schema, chunking, metadata, and content lifecycle, not copying files into a new location.

•       Create a shared, machine-readable source of design and accessibility rules so multiple tools can apply the same standard consistently.

•       Implement persistent identifiers, citations, and provenance so a generated design or code artifact can be traced to its originating research finding, rule, component, and workflow step.

•       Design and build agent connections to live design files, research repositories, codebases, and enterprise systems using MCP or comparable tool-integration patterns.

•       Automate handoffs into systems such as GitHub and Jira, including submission for engineering review and integration with the organization’s knowledge-management platform.

•       Instrument the toolchain so leaders and delivery teams can understand cycle time, quality scores, human-review rounds, exceptions, adoption, and research traceability.

•       Build the technical mechanisms for quality checks, false-positive tuning, overrides, audit trails, and release gates. The paired AI Design Engineer defines the design-facing meaning and acceptance threshold of those checks.

•       Map the target architecture, dependencies, service boundaries, knowledge locations, and skill registry; present viable options and gain stakeholder agreement before implementation.

•       Write production code, establish automated build and release pipelines, and create playbooks that allow the operating model to survive changes in tools or models.

Required qualifications

•       Recent hands-on experience building an agentic AI application, reusable agent capability, or multi-step workflow used by a team—not only experimenting with prompts or chat interfaces.

•       Strong production software-engineering skills in Python and TypeScript, including APIs, integrations, testing, maintainability, and secure handling of enterprise data.

•       Experience structuring unstructured information for reliable AI and human retrieval using taxonomy, schema design, metadata, persistent identifiers, and content lifecycle practices.

•       Hands-on experience with RAG or comparable grounding patterns, including retrieval quality, citations, source freshness, and failure handling.

•       Experience with MCP or equivalent tool and data-source integration patterns for connecting AI agents to repositories, applications, and live knowledge.

•       CI/CD and workflow-automation experience using GitHub Actions, GitLab CI, or equivalent tooling.

•       Instrumentation and observability experience, including events, logs, metrics, audit trails, and dashboards that explain what an automated workflow did.

•       Ability to create clear architecture and process diagrams, facilitate decisions across design, engineering, and business stakeholders, and then implement the agreed approach.

•       Ability to operate independently: present options with trade-offs, recommend a path, keep stakeholders informed, and continue executing without detailed daily direction.

•       Strong peer-collaboration habits. This role must work as a tightly coupled partner to the AI Design Engineer and share decisions without territorial ownership.

Preferred experience

•       Experience with current AI coding or agent environments such as Claude Code, Codex, ChatGPT Work, or comparable platforms.

•       Familiarity with the structure of Figma files, design tokens, component libraries, or Figma plugin code.

•       Design-linting, static-analysis, policy-as-code, or rules-engine experience.

•       Working knowledge of Jira, Confluence, GitHub, and enterprise knowledge-management platforms and their data models or APIs.

•       Microsoft Copilot Studio or comparable low-code automation experience.

•       Experience in insurance, financial services, healthcare, or another highly regulated environment.

Experience taking an AI or knowledge platform from prototype to team or enterprise adoption