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AI Agent Architect, Customer Experience at Airtable | AINative.careers

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Join Airtable as a 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.
Own the feedback loop. Monitor the observability layer that turns agent behavior into actionable insights. Instrument retrieval accuracy, action success rates, and failure patterns.
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.
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.

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 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 can explain complex AI system behavior to non-technical stakeholders, write clear technical documentation, and translate business requirements into system specifications.