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.
16 postings · 10 distinct titles · from 260,470 real job postings · see the live data →
What the postings ask this role to do
322 tasks extracted from real AI Validator job descriptions, classified Automate / Augment / Human-only. Only 1.9% 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
Compose your org's AI Validator job description
Start from the tasks real postings ask for, keep the ones that match your operation, add what is specific to you. The tasks carry their AI classification, so the JD you take away already says what AI runs and what stays with people.
Start with the work, not the org chart.
Run the audit on one operation and see what this role would own first.