AI Evaluation Specialist
Evaluation and engineering, a mid-level role
What does AI Evaluation Specialist do?
Designs and runs the tests that show how an AI system behaves under realistic, difficult and adversarial conditions, and helps teams decide whether results support launch, restriction, remediation or rejection.
What it decides: What a score measures, what it misses, and whether the evidence supports release.
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 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
- AI security fundamentalsrequired, working knowledge
Understands prompt injection, data poisoning, model theft, insecure integrations and excessive agent privileges, and the controls that reduce each.
17 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 contestabilitypreferred, 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
- Agentic AI controls and authorization boundariespreferred, working knowledge
Governs AI agents that take actions: tool access, least privilege, interruptibility, cascading actions and accountability for what an agent did.
21 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.
- Certified AI Practitioner: Workplace Foundations31 topics
- Certified AI Governance Professional (CAIGP)22 topics
- Certified Agentic AI Governance Professional (CAAGP)14 topics
- Certified AI Data Governance Professional (CADGP)12 topics
- EU AI Act Implementation Expert11 topics
- The AI Lobbyist: Certified AI Policy Strategist8 topics
- Certified AI Transformation Professional (CATP)7 topics
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Roles that feed into it
- QA Engineer
- Data Scientist
- Research Analyst
- Trust and Safety Specialist
- AI Model Validator
- Domain expert with analytical skills
Where it leads
- Evaluation Lead
- AI Assurance Manager
- Model Risk Manager
Backgrounds that reach it fastest
What postings tend to name
Frameworks: NIST AI RMF MEASURE, NIST AI 600-1.
Credentials often listed: 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 can conventional software testing not cover AI?
- Outputs vary by prompt, context, data, user and model version. Evaluation has to describe a probabilistic system with scenarios, rubrics, thresholds and subgroup tests, and say plainly what the number leaves out.