AI Agent Architect, Customer Experience
Customer Success
WFA Digital Insight
Demand for AI and machine learning specialists in customer experience has surged, with skills in large language models and AI agent architectures in high demand. Airtable, a leader in no-code app platforms, offers a unique opportunity to shape the future of AI-native customer support. Before applying, candidates should understand the evolving landscape of AI in customer experience and be prepared to showcase their expertise in AI agent design and optimization.
Job Description
About the Role
Airtable is seeking an experienced AI Agent Architect to own the technical foundation of its AI-native customer support experience. The ideal candidate will have deep fluency in large language models and hands-on experience with AI agent architectures.Responsibilities
- Own Agent retrieval accuracy and relevance: Architect knowledge systems for AI agents to surface the right answer on the first try.
- Drive automated resolution rates: Build decision frameworks for confident actions, accessing necessary APIs and encoding business logic.
- Manage AI safety and trust: Establish guardrails for high resolution rates and low failure rates, preventing unintended behaviors.
- Own the feedback loop: Monitor the observability layer for actionable insights and drive week-over-week improvements in agent performance.
- Continuously improve agent quality: Develop prompt architectures, versioning, A/B testing, and performance evaluation for consistency, accuracy, and adaptability.
How to Stand Out
- Showcase your experience with large language models and AI agent architectures in your resume and cover letter.
- Prepare to discuss your approach to designing and optimizing AI agent architectures for customer support.
- Highlight any experience you have with integrating AI agents with external systems, such as billing platforms or CRMs.
- Be prepared to walk through your process for encoding business logic into AI agent decision frameworks.
- Consider sharing examples of how you've driven improvements in agent performance through data-driven insights.
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