AI Validator. The role, the market signal, and how to build it in your org.
The assurance layer for AI work. Checks that model and agent outputs are correct, safe, and compliant before they ship, the way QA checks software. Cross-functional: every team putting AI into production needs someone validating what it produces.
In a workforce of people and AI agents, this role takes shape around the workflows it supports. Define its responsibilities and handoffs, then prepare people to perform that work.
17 postings · 11 distinct titles · from 264,613 real job postings · see the live data →
What the postings ask this role to do
330 tasks extracted from real AI Validator job descriptions, classified Automate / Augment / Human-only. Only 1.8% can be fully automated: companies are hiring this role for the judgment, not the keystrokes.
- Document test cases.
- Execute tests on genai pipelines and workflows.
- Generate synthetic test data for qa validation.
- Automate web services using soapui and/or python.
- Create automated test scripts using the pytest framework.
- Perform api automation testing.
- Test ai applications with a focus on gen ai testing.
- Analyze test results to identify defects and root causes.
- Automate tests using test automation frameworks and tools.
- Use programming languages such as python, java, or javascript to support testing activities.
- Manage multiple testing tasks and priorities in a fast-paced environment.
- Collaborate with team members to support testing activities.
- Collaborate with team members to plan and execute testing activities.
From the market's version of this role to your version of it
Start with the work, not the org chart.
Define what this role will own, then connect those responsibilities to practice and assessment.