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AI Model Validator

Evaluation and engineering, a mid-level role

What does AI Model Validator do?

Independently checks whether a model is conceptually sound, implemented correctly, performing adequately and suitable for its intended use, beyond rerunning the developer's tests.

What it decides: Whether the evidence is sufficient, what the severity of each finding is, and under what conditions a model may be used.

The competencies employers name

  • AI evaluation and testing designcore, depth expected

    Designs tests for factuality, robustness, fairness, safety and abuse resistance with rubrics, baselines and thresholds, and says what a score misses.

    12 graded topics teach this

  • Model risk management and independent challengecore, depth expected

    Classifies models by tier, sets validation requirements, challenges data, methodology and performance evidence, and reports aggregate exposure.

    14 graded topics teach this

  • Model failure modes and bias recognitioncore, depth expected

    Recognizes hallucination, drift, skew, brittleness and biased outcomes, and knows how each one enters a system.

    10 graded topics teach this

  • How models work, at a governance depthcore, depth expected

    Explains training, tokens, context windows, embeddings, retrieval and fine-tuning well enough to ask an engineer a precise question and spot weak evidence.

    18 graded topics teach this

  • Data quality rules and monitoringrequired, working knowledge

    Profiles data, sets rules and thresholds, monitors, assigns issues to owners, and knows when data is not fit to train or run a model.

    15 graded topics teach this

  • Post-deployment monitoring and drift detectionrequired, working knowledge

    Sets performance metrics, thresholds and review triggers after launch, and treats a model change, a vendor update or new data as a reason to re-check.

    12 graded topics teach this

  • Evidence collection and audit-ready documentationrequired, working knowledge

    Collects, labels and preserves the evidence that a control operated, a decision was made, and a claim can be defended to an auditor or regulator.

    20 graded topics teach this

  • Explainability, transparency and contestabilityrequired, working knowledge

    Decides what a person affected by an AI decision must be told, how an output can be explained, and how they can challenge it.

    11 graded topics teach this

  • Human oversight designpreferred, working knowledge

    Defines who reviews AI outputs, what they check, when they can override, and how to keep review from becoming a rubber stamp.

    9 graded topics teach this

Where it is taught

Counted from the graded topics that teach this role's competencies. Your own path is shorter: it skips what you already cover.

Check your readiness for this role

Add what you already have (optional)
Signed in? Every topic you have passed already counts as proof.

Roles that feed into it

  • Data Scientist
  • Quantitative Analyst
  • QA Engineer
  • Machine Learning Evaluator
  • Technology Auditor

Where it leads

Backgrounds that reach it fastest

What postings tend to name

Frameworks: SR 11-7, NIST AI RMF MEASURE.

Credentials often listed: FRM, PRM, AIGP. GAGE does not issue these and does not prepare for their exams; the record you earn here is your own graded evidence, which stands beside them.

Questions

Why does validation have to be independent?
The team that builds or buys a model should not be the only team deciding whether its evidence is sufficient. Independence is what makes the conclusion worth anything.
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