Certified AI Expert (CAIE)

Who should get certified

CAIE is intended for candidates in advanced technical AI roles who want formal recognition of their ability to build and operate complex AI systems in real environments.

Typical candidates include:

• Machine Learning Engineers and AI Engineers responsible for end-to-end model development and deployment.
• Senior Data Scientists and Applied Scientists leading complex modeling and experimentation projects.
• MLOps Engineers and ML Platform Engineers who design and run robust infrastructure for training, deployment, and monitoring.
• AI Solutions Architects, Technical Product Owners, and Engineering Managers with strong hands-on AI skills who drive AI system design and implementation.
• Researchers and advanced practitioners translating cutting-edge AI methods into scalable, production-grade systems.

The exam is not intended for:

• Pure business or non-technical roles focused mainly on using AI tools (better served by the Certified AI Practitioner – CAIP certification).
• Beginners without hands-on experience in ML coding, model training, and deployment.
• Candidates whose work is purely theoretical with no applied, system-level responsibilities.

What does CAIE cover

The CAIE exam assesses advanced, applied skills across five domains. All questions are scenario-based or conceptually rich, focusing on realistic trade-offs, constraints, and system-level reasoning rather than memorizing formulas.

• Domain 1 – Advanced Machine Learning & Deep Learning (25%)
  Design and evaluation of advanced architectures (such as transformers and other generative models), training and fine-tuning workflows, optimization techniques, and robust validation strategies across different tasks and data conditions.
• Domain 2 – AI Model Deployment, MLOps & RAG (25%)
  End-to-end ML pipelines, data and model versioning, experiment tracking, cloud-native deployment patterns, monitoring and drift detection, and the design and evaluation of retrieval-augmented generation (RAG) systems.
• Domain 3 – AI for Decision-Making & Reinforcement Learning (20%)
  Formulating sequential decision problems, choosing between RL and simpler alternatives, understanding core RL algorithms, and designing AI-driven automation with appropriate human-in-the-loop control and guardrails.
• Domain 4 – Ethical, Explainable & Trustworthy AI (15%)
  Applying trustworthy AI principles, using explainable AI (XAI) techniques, analyzing and mitigating bias, and aligning AI deployments with regulatory, legal, and governance expectations.
• Domain 5 – Emerging AI Trends & System-Level Design (15%)
  Positioning foundation and multimodal models, agentic and tool-augmented systems, and other frontier techniques within robust architectures, while planning AI roadmaps and risk management strategies over realistic time horizons.

Why get certified?

Earning the CAIE certification helps you:

• Demonstrate expert-level proficiency in designing, implementing, and operating advanced AI systems end to end.
• Stand out in the job market with an independent, vendor-neutral validation of your advanced AI skills.
• Bridge the gap between research and engineering by showing that you can translate cutting-edge methods into reliable, scalable systems.
• Strengthen your profile for roles such as Senior ML Engineer, AI Engineer, Senior Data Scientist, AI Solutions Architect, MLOps Engineer, or AI Engineering Manager.
• Position yourself as a trusted technical partner for business, compliance, and leadership teams when planning and deploying AI initiatives.
• Build a foundation for future AIPROI certifications as the program expands, and help keep your other AIPROI credentials current where applicable.

Recommended knowledge and experience

There are no formal mandatory prerequisites for CAIE. However, candidates are much more likely to succeed if they meet the recommended profile below.

Formal / educational foundations

• Completion of AI foundations or AI practitioner-level content (for example, successful completion of the Certified AI Practitioner – CAIP exam or equivalent knowledge).
• An applied AI or AI developer course covering practical model training, evaluation, and basic deployment.
• An advanced AI / AI expert course such as Advanced Artificial Intelligence, aligned with the CAIE syllabus.
• Strong programming skills in Python, including use of common ML and deep learning libraries (such as PyTorch, TensorFlow, and scikit-learn).
• Prior exposure to calculus-based probability and statistics (random variables, expectations, distributions, hypothesis testing) and linear algebra (vectors, matrices, eigenvalues, optimization concepts).

Professional experience

• At least 2–3 years of practical experience in data science, ML engineering, or closely related roles.
• Hands-on experience building and tuning ML and deep learning models on real datasets using at least one deep learning framework.
• Experience using cloud platforms or similar environments for ML (such as managed ML services, containerized deployments, or cloud notebooks).
• Familiarity with source control (for example, Git), experiment tracking, and basic CI/CD workflows.
• Participation in at least one end-to-end AI or ML project that progressed beyond a prototype into a maintained system, even if small scale.

Language skills

• The exam is delivered in English.
• B2-level English (reading comprehension) or higher is strongly recommended, as questions may include short scenarios, diagrams, and technical terminology.

How to Get Certified?

Follow these steps to earn your CAIE certification:

1. Build your skills
   Complete the Advanced Artificial Intelligence course aligned with CAIE or an equivalent advanced AI program. Strengthen your experience with end-to-end ML projects, MLOps pipelines, RAG systems, RL experiments, and XAI and bias analysis.
2. Review the exam blueprint and policies
   Study the CAIE exam domains and objectives and review the latest AIPROI Exam & Retake Policy, Candidate Agreement, Privacy and Data Protection Notice, and Certification Code of Ethics.
3. Register and schedule your exam
   When registration is open, schedule your remote, proctored exam via the AIPROI Exam Portal or an AIPROI Approved Exam Provider. Choose a suitable date and time, confirm technical requirements, and submit any accommodation requests in advance if needed.
4. Take the exam and meet all requirements
   Sit the online, proctored exam, follow all security and identification procedures, and achieve the passing scaled score of 750 or higher.

Once you pass the exam and all verification checks are completed, you will be awarded the Certified AI Expert (CAIE) credential.

Preparation resources

To prepare effectively for CAIE, we recommend:

• Advanced Artificial Intelligence – official AIPROI course
  The primary course aligned with the CAIE domains, available in instructor-led and self-paced formats depending on the training provider.
• Hands-on projects
  Implement substantial projects that cover at least some of the following: fine-tuning transformer-based or other advanced models, end-to-end MLOps pipelines, RAG workflows over realistic document collections, reinforcement learning in a non-trivial environment, and XAI and bias analysis on models you have built.
• Official CAIE exam guide and supporting materials
  Use the CAIE Exam Guide and, when available, official Student Guides and practice exams or question sets to familiarize yourself with the exam structure and types of questions.
• Supplementary reading and references
  Deep learning, reinforcement learning, and ML systems design textbooks and reputable online materials, as well as vendor-neutral resources on trustworthy AI, XAI, and AI governance.

Focus on mastering concepts, patterns, and trade-offs rather than memorizing the details of any single tool, framework, or cloud provider.

Exam details

Key information about the CAIE exam:

• Certification title: Certified AI Expert (CAIE)
• Exam code: AIP-CAIE-300
• Question format: Multiple-choice (single answer) and multiple-response (select all that apply), with many questions built around realistic project scenarios, system diagrams, or experimental results. No coding during the exam.
• Number of questions: 80 scored questions
• Exam duration: 180 minutes of standard testing time
• Language: English
• Prerequisites: No formal prerequisites. Strongly recommended: CAIP and/or AI developer-level knowledge plus the background described in the “Recommended knowledge and experience” section.
• Delivery method: Remote, online, proctored exam delivered via an AIPROI Approved Exam Provider using a secure exam client or browser.
• Passing score: 750 out of 1000 on a scaled score range (0–1000). Scores are scaled so that the passing standard remains consistent across different exam forms.
• Scoring and reporting: You receive a scaled score and pass/fail result, plus domain-level performance indicators where available. Scores are equated across forms to account for small differences in difficulty.
• Certification validity: 2 years from the date of certification issue.
• Retake policy (summary): Minimum 14-day waiting period after a failed attempt and up to three attempts in any rolling 12-month period, according to the current AIPROI Exam & Retake Policy.
• Accommodations: Additional 30 minutes of exam time may be available for non-native English speakers, and other accommodations may be available for eligible candidates on request, in line with the AIPROI Exam & Retake Policy and accessibility guidelines.

Use of AI tools during the exam is strictly prohibited, in line with the AIPROI Exam Security Policy and Certification Code of Ethics.

Exam availability

CAIE is delivered as a remote, online, proctored exam with global availability through the AIPROI Exam Portal and AIPROI Approved Exam Providers.

• Sign-ups for this exam are planned to start in January 2026.
• Exact registration dates, available time slots, and supported time zones are published in the AIPROI Exam Portal and by AIPROI Approved Exam Providers.
• Additional exam windows and language options may be introduced over time. Always refer to the latest information published by AIPROI and its providers.