Airtree

AI Engineer

• Define evaluation problems: success criteria, failure modes, datasets, labelling guidelines, and score functions.
• Build and maintain an evaluation harness: regression tests, edge-case suites, and quality dashboards to prevent backsliding.
• Implement workflow systems end-to-end (data → model/LLM components → post-processing → acceptance testing) until they pass eval thresholds.
• Partner with product and domain stakeholders to translate messy real-world requirements into testable specs.

Founding Applied AI Engineer – Eval-Driven