Aizhan Azhybaeva

Aizhan Azhybaeva is an AI/ML QA engineer at aiml.qa - the global pure-play AI/ML Quality Assurance practice making AI reliable, safe, and regulation-ready for Series A-C startups.

Focus

Aizhan publishes original research and practitioner guidance on:

• LLM evaluation frameworks - DeepEval, RAGAS, Promptfoo, Braintrust, LangSmith, Arize Phoenix, Weights & Biases Weave, TruLens
• Hallucination benchmarking for production LLMs across RAG and agent use cases
• Model validation methodology - statistical validation, fairness auditing, distributional robustness testing
• AI red-teaming techniques - prompt injection, tool poisoning, jailbreak surface mapping
• Regulatory AI evaluation - EU AI Act Article 15, CBUAE AI Guidance, NIST AI RMF, ISO/IEC 42001, FDA SaMD
• MLOps pipeline testing - CI/CD for ML, model registry validation, inference infrastructure testing

Research published under this byline is collaborative work produced by the aiml.qa team, reviewed for technical and regulatory accuracy before publication.

Topics Covered

• AI Quality Assurance
• LLM Evaluation
• RAGAS
• DeepEval
• Promptfoo
• Braintrust
• LangSmith
• Arize Phoenix
• Hallucination Benchmarking
• Model Validation
• Fairness and Bias Auditing
• Distribution Shift Detection
• AI Red-Teaming
• Prompt Injection Testing
• MLOps Pipeline Testing
• EU AI Act Article 15
• CBUAE AI Guidance
• NIST AI RMF
• FDA SaMD Evaluation

Profiles & Links

• https://www.linkedin.com/company/nomadx-ae
• https://github.com/nomadx-ae