💡 A comprehensive, community-curated collection of 185+ interview questions and answers for AI Biotech Regulatory / GxP Liaison roles — professionals who bridge AI/ML engineering teams and regulatory/quality functions to ensure AI-enabled biotech, pharma, and diagnostic products meet GxP, FDA/EMA, and data integrity requirements while remaining commercially viable to build and ship.
AI Biotech Regulatory / GxP Liaisons sit at the intersection of regulatory affairs, quality assurance, and AI/ML product development. They translate regulatory requirements (GxP, 21 CFR Part 11, SaMD, GMLP) into concrete engineering/validation practices, and translate technical AI/ML realities (probabilistic outputs, continuous learning, data drift) into regulatory strategy that satisfies FDA, EMA, and other health authorities.
- ✅ GxP fundamentals (GCP, GMP, GLP, GVP) as applied to AI systems
- ✅ FDA/EMA AI/ML regulatory frameworks (SaMD, PCCP, GMLP, EU AI Act)
- ✅ Computer System Validation (CSV/CSA) and 21 CFR Part 11 for ML systems
- ✅ AI model lifecycle governance and change control
- ✅ Data integrity, provenance, and ALCOA+ for training/validation data
- ✅ Quality Management Systems (QMS) integration for AI/ML development
- ✅ Inspection readiness and regulatory submission support
- ✅ Cross-functional liaison skills between engineering and regulatory/QA
⏱️ Estimated preparation time: 30–50 hours 🎙️ Interview duration: Typically 4–6 rounds (3–5 hours total), often including a scenario-based regulatory strategy round
📦 Awesome-AI-Biotech-Regulatory-GxP-Liaison-Interview-QA/
├── 📄 README.md
├── 📄 CONTRIBUTING.md
├── 📄 LICENSE
├── 🖼️ assets/
│ └── 🎨 banner.svg
├── 📚 topics/
│ ├── 01-GxP-Fundamentals-for-AI.md
│ ├── 02-FDA-EMA-AI-ML-Regulatory-Frameworks.md
│ ├── 03-Computer-System-Validation-Part11.md
│ ├── 04-AI-Model-Lifecycle-Governance.md
│ ├── 05-Data-Integrity-Provenance.md
│ ├── 06-Quality-Management-Systems.md
│ ├── 07-Risk-Management-AI-Systems.md
│ ├── 08-Regulatory-Submission-Strategy.md
│ ├── 09-Inspection-Readiness-Audits.md
│ ├── 10-Cross-Functional-Liaison-Skills.md
│ ├── 11-Troubleshooting-Case-Studies.md
│ └── 12-Industry-Context-Evolving-Guidance.md
├── 📖 docs/
│ ├── glossary.md
│ ├── resources.md
│ └── roadmap.md
└── 🚫 .gitignore
| # | 📘 Topic | 🔬 Focus Area | 📝 Q&A Count |
|---|---|---|---|
| 01 | 🏛️ GxP Fundamentals for AI | GCP/GMP/GLP/GVP applied to AI/ML systems | 16 |
| 02 | 🏥 FDA/EMA AI/ML Regulatory Frameworks | SaMD, PCCP, GMLP, EU AI Act | 17 |
| 03 | 🖥️ Computer System Validation & Part 11 | CSV/CSA for ML pipelines, e-signatures | 16 |
| 04 | 🔄 AI Model Lifecycle Governance | Versioning, retraining, change control | 16 |
| 05 | 🔐 Data Integrity & Provenance | ALCOA+ for training/validation data, lineage | 15 |
| 06 | 📋 Quality Management Systems | QMS integration, SOPs for AI/ML development | 15 |
| 07 | ISO 14971, AI-specific risk frameworks | 15 | |
| 08 | 📑 Regulatory Submission Strategy | eSTAR, 510(k)/PMA/De Novo AI documentation | 15 |
| 09 | 🔍 Inspection Readiness & Audits | FDA BIMO, notified body audits for AI systems | 15 |
| 10 | 🤝 Cross-Functional Liaison Skills | Translating between engineering and regulatory | 15 |
| 11 | 🛠️ Troubleshooting & Case Studies | Validation failures, audit findings, remediation | 15 |
| 12 | 🌐 Industry Context & Evolving Guidance | Guidance evolution, market trends, harmonization | 14 |
| 🏆 TOTAL | 184 |
| 📅 Week | 📚 Topics | 🎯 Focus |
|---|---|---|
| Week 1 | Topics 01–02 | 🏛️ GxP Fundamentals + 🏥 FDA/EMA Frameworks |
| Week 2 | Topics 03–04 | 🖥️ CSV/Part 11 + 🔄 Model Lifecycle Governance |
| Week 3 | Topics 05–06 | 🔐 Data Integrity + 📋 QMS |
| Week 4 | Topics 07–08 | |
| Week 5 | Topics 09–10 | 🔍 Inspection Readiness + 🤝 Liaison Skills |
| Week 6 | Topics 11–12 + Review | 🛠️ Case Studies + 🌐 Industry Context + 🎤 Mock Interviews |
💬 From Topic 04: AI Model Lifecycle Governance
❓ Q: An ML engineering team wants to retrain a deployed diagnostic model weekly using newly accumulated data. What's your role as the regulatory/GxP liaison in enabling this safely?
💡 A: The core task is determining whether weekly retraining falls within a pre-authorized Predetermined Change Control Plan (PCCP) scope, or whether it requires new submission review. My role is to work backward from the desired retraining cadence during initial submission planning — defining the SaMD Pre-Specifications and Algorithm Change Protocol broadly enough to cover routine retraining on same-distribution data, with explicit, pre-validated performance/safety acceptance criteria that must be met before each retrained version can be deployed. I'd also establish the internal change-control SOP and validation evidence package (regression testing against a locked eval set, documented sign-off) that operationalizes the PCCP day-to-day, so engineering can move at their desired cadence without each cycle becoming an ad hoc regulatory question.
See CONTRIBUTING.md for guidelines.
🌟 Areas seeking contributions:
- 🇪🇺 EU AI Act practical implementation case studies
- 🌍 Region-specific frameworks (PMDA, NMPA, Health Canada)
- 🤖 Large language model (LLM)-specific regulatory considerations in GxP contexts
- 🔬 De-identified audit finding / CAPA case studies
MIT License — see LICENSE.
📅 Last Updated: July 2026 | 👥 Contributors: 1 (growing!) | ⭐ Star this repo if it helps!
Made with ❤️ for the AI Biotech Regulatory community