Why This Job is Featured on The SaaS Jobs
This role sits at the intersection of SaaS customer experience and applied AI systems design, where support is increasingly delivered through AI-native workflows rather than traditional ticket handling. At a product-led platform company like Airtable, the mandate to architect agent reasoning, retrieval, and actioning reflects a broader shift in SaaS toward automation that must remain reliable, auditable, and tightly integrated with core product data.
For a long-term SaaS career, the work builds durable expertise in operating LLM-based systems as production infrastructure. Ownership of evaluation metrics, observability, and iterative improvement mirrors how modern SaaS teams manage uptime and quality, but applied to agent behavior and risk. The integration focus also strengthens a key SaaS competency: connecting customer-facing automation to billing, CRM, and internal tooling while maintaining data integrity and controlled permissions.
The position will suit someone who prefers systems thinking over one-off prototyping, and who is comfortable translating ambiguous CX problems into measurable technical levers. It aligns well with professionals who have prior exposure to platform engineering, solutions architecture, or technical program work and want to specialize in AI governance, RAG design, and cross-functional delivery within a remote-first operating model.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Join Airtable as an CX AI Architect and own the technical foundation that powers our AI-native customer support experience. You'll design and optimize how our AI agents reason, retrieve, decide, and act—architecting the knowledge systems, decision logic, and guardrails that enable reliable, scalable AI resolution at scale. This role requires deep fluency in how large language models work, hands-on experience with AI agent architectures, and the ability to partner closely with Engineering on production systems.
What you'll do
- Own Agent retrieval accuracy and relevance. Architect the knowledge systems that enable AI agents to surface the right answer on the first try. Measure and improve retrieval precision, contextual relevance, and hallucination rates.
- Drive automated resolution rates. Build the decision frameworks that allow agents to take confident actions. What APIs do agents need to access? When can they make account modifications? You're accountable for encoding business logic into auditable, predictable systems that resolve issues without human intervention.
- Manage AI safety and trust. Establish the guardrails that keep resolution rates high while failure rates stay low. You're responsible for what the agent doesn't do wrong: edge cases caught, prompt injection blocked, unintended behaviors prevented.
- Own the feedback loop. Monitor the observability layer that turns agent behavior into actionable insights. Instrument retrieval accuracy, action success rates, and failure patterns. Use this data to drive measurable week-over-week improvements in agent performance.
- Continuously improve agent quality. Develop and maintain the prompt architecture that governs how agents reason and respond. Build systematic approaches to versioning, A/B testing, and performance evaluation, measuring consistency, accuracy, and adaptability across scenarios.
- Drive integration strategy. Architect how agents connect to external systems—billing platforms, CRMs, internal tools, Airtable APIs. Define authentication patterns, error handling, and data transformation. Uptime, error rates, and data accuracy are your metrics.
Who you are
- You understand how large language models work—not just how to use them, but how they reason, where they fail, and why. You're familiar with concepts like RAG architectures, prompt engineering patterns, chain-of-thought reasoning, and agent frameworks. You've built or significantly contributed to AI-powered systems in production.
- You think in terms of data flows, state management, error handling, and edge cases. You can design complex systems that are both powerful and reliable. You've likely worked in roles like solutions architecture, platform engineering, or technical program management.
- You can write scripts, work with APIs, query databases, and prototype solutions. You're not a full-time software engineer, but you're dangerous enough to build, test, and validate technical approaches independently.
- You instrument systems, analyze logs, and use data to diagnose issues and validate improvements. You build dashboards, define metrics, and can tie technical changes to business outcomes like resolution rates and customer satisfaction.
- You can explain complex AI system behavior to non-technical stakeholders, write clear technical documentation, and translate business requirements into system specifications. You're effective working across engineering, product, and operations teams.
Airtable is an equal opportunity employer. We embrace diversity and strive to create a workplace where everyone has an equal opportunity to thrive. We welcome people of different backgrounds, experiences, abilities, and perspectives. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status or any characteristic protected by applicable federal and state laws, regulations and ordinances. Learn more about your EEO rights as an applicant.
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If you have a medical condition, disability, or religious belief/practice which inhibits your ability to participate in any part of the application or interview process, please complete our Accommodations Request Formand let us know how we may assist you. Airtable is committed to participating in the interactive process and providing reasonable accommodations to qualified applicants.
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