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[Remote] AI Agent Architect, Customer Experience
Note: The job is a remote job and is open to candidates in USA. Airtable is a no-code app platform that empowers organizations to accelerate their business processes. In this role, you will own the technical foundation for AI-native customer support experiences, focusing on architecting knowledge systems and decision logic for AI agents.
Responsibilities
- 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
Skills
- Deep fluency in how large language models work
- Hands-on experience with AI agent architectures
- Ability to partner closely with Engineering on production systems
- Experience in architecting knowledge systems for AI agents
- Ability to measure and improve retrieval precision, contextual relevance, and hallucination rates
- Experience in building decision frameworks for AI agents
- Ability to encode business logic into auditable, predictable systems
- Experience in managing AI safety and trust
- Ability to establish guardrails for AI agents
- Experience in monitoring observability layers for agent behavior
- Ability to instrument retrieval accuracy, action success rates, and failure patterns
- Experience in developing and maintaining prompt architecture for AI agents
- Ability to build systematic approaches to versioning, A/B testing, and performance evaluation
- Experience in architecting integration strategies for AI agents
- Ability to define authentication patterns, error handling, and data transformation
- Understanding of data flows, state management, error handling, and edge cases
- Experience in designing complex systems that are powerful and reliable
- Experience in roles like solutions architecture, platform engineering, or technical program management
- Ability to write scripts, work with APIs, query databases, and prototype solutions
- Ability to instrument systems, analyze logs, and use data to diagnose issues
- Experience in building dashboards and defining metrics
- Ability to explain complex AI system behavior to non-technical stakeholders
- Ability to write clear technical documentation
- Ability to translate business requirements into system specifications
- Effective collaboration across engineering, product, and operations teams
Benefits
- Our total compensation package also includes the opportunity to receive benefits, restricted stock units, and may include incentive compensation.
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