AI Tester-BSK 2nd stage-WFO-RAG test,chatbot-2nd Round F2F

• Build and maintain the coverage matrix across design types and symbol classes, flagging thin or empty cells as known gaps
• Report accuracy per symbol class with confidence intervals, not just an aggregate figure, and enforce the per-class minimum instance count below which a class is reported as provisional
• Run no-regression checks across fine-tune rounds so a new model cannot pass on average while regressing on a rare but critical class
• Build the Knowledge Bot evaluation suite: a curated Q&A set with known-correct answers, scored for groundedness and faithfulness against every prompt and model version
• Perform adversarial and AI security testing before production and after material architectural change prompt injection, jailbreak attempts, RAG-specific attacks, cross-user data isolation and AI data leakage
• Validate correction-quality controls: spot-audit ambiguous classes, track inter-reviewer agreement, and confirm conflicting human corrections are adjudicated before entering the labelled set
• Stand up drift monitoring and produce the periodic model-health report covering drift indicators, correction rates, confidence-score trends, retraining events and acceptance results by version
• Run functional and integration testing of the HE2 workflow alongside the model validation work

What you need

• Python, and the ability to write your own evaluation harnesses rather than only driving a test tool
• Solid grasp of ML evaluation: precision, recall, per-class metrics, confidence intervals, sampling bias, and why a held-out set must stay held out
• Experience testing an ML or AI system in production — you have caught a model problem, not only an application bug
• Test automation and CI, with pytest or equivalent
• API testing and the discipline to build reproducible, versioned test datasets
• Clear written reporting: your reports go to the customer and support a payment gate

Nice to have

• LLM red-teaming or prompt injection testing experience
• Familiarity with RAG evaluation frameworks and groundedness scoring
• Statistical background: clustering effects, effective sample size, stratified sampling
• Exposure to SageMaker Model Monitor, MLflow or comparable drift and experiment tracking

Security testing certification or demonstrable practice