Become a Chartered AI Engineering Professional (CAiEP^®)

Minimum Qualifications:

• A bachelor’s degree in a relevant field such as Computer Science, Data Science, Engineering, Mathematics / Statistics, Applied Sciences, Information Technology

Experience Requirements:

• Minimum 7–10 years of professional experience in AI, ML, or data engineering roles.
• Demonstrated proficiency in:
  
  • Machine learning model development
  • AI system architecture or deployment
  • Use of programming languages (Python, R, etc.)
  • Familiarity with frameworks like TensorFlow, PyTorch, Scikit-learn, etc.

Ideal Candidates:

• Data scientists, AI engineers, ML developers, research engineers, etc.
• Professionals working on AI/ML pipelines, model development, or production deployment.
• Certified professionals with valid AiE^®, SBDE^™, SDS^™ credentials.

Minimum Qualifications:

• A Master’s or PhD in a relevant domain Artificial Intelligence, Machine Learning, Computer Vision / NLP, Computational Science, Advanced Analytics / Applied Mathematics

Experience Requirements:

• Minimum 5–7 years of hands-on experience in AI system development, research, or engineering roles.
• Experience with:
  
  • Applied AI in production or research environments
  • Model evaluation, tuning, and scalability
  • Cross-functional collaboration in AI product teams

Ideal Candidates:

• Early- to mid-career AI professionals with advanced academic training.
• AI researchers moving into applied engineering roles.
• PhDs transitioning to industry.

Minimum Qualifications:

• No strict academic requirements.
• Candidates must demonstrate outstanding achievement in the AI engineering or data science space.

Eligibility Based On:

• Extensive, self-taught or unconventional AI development experience.
• Proven record of leadership or innovation in AI systems or platforms.
• Contributions to the AI/ML ecosystem (open-source tools, notable models, community leadership).

Required Evidence (minimum two of the following):

1. Published or deployed AI solutions at scale (codebases, GitHub repos, published libraries, etc.).
2. Awards, recognitions, or keynotes in AI development.
3. Endorsements from AI thought leaders or tech executives.
4. Media coverage, patents, or significant research contributions.

Review Process:

• Application reviewed by a Technical Peer Review Board.
• May require a technical interview or demonstration.
• Assessment modules (exam, project) are still mandatory to certify capability.

Ideal Candidates:

• Self-taught engineers, open-source contributors, AI startup founders, or community-recognized AI builders.
• Professionals without formal degrees but with proven, published, or deployed AI systems.