Chartered AI Engineer (CAIE)

Learn more about AI Engineer Level 2

AI Engineer Level 2

Working professionals who are team leads for AI engineers. They are able to manage projects as well as business stakeholders, both internal and external. They can see how the projects fit into the department’s business process and needs and are able to set up the appropriate process to monitor the effectiveness of the AI models that are implemented. They can architect the necessary AI modules to ensure successful deployment of machine learning models.

Assessment Rubrics for Chartered AI Engineer Level 2

1. Project Management Assessment

  • Display good understanding of project development process
  • The benefits achieved from the deployment are detailed clearly, together with objective evidence of success as well as considerations for future needs and/or scalability
  • Assumptions for the project (including models) to perform are well articulated, with built-in plans to discover and recover.
  • Able to manage data-to-insight latency to meet application requirement
  • There is ongoing evaluation of the model performance according to a clear set of metrics,
  • Improvement plans have been thought up with reported issues being consistently managed
  • Understand who are the end users and able to explain how the AI solution will benefit them, and manage their expectations
  • Able to explain both the business impact including both  business value and risk of the AI solution
  • Able to work effectively with business stakeholders and take in their input to co-develop AI solutions and drive the subsequent business adoption.
  • Able to educate business stakeholders and add value to the business proposition of the AI solution during the co-development

2. Solution Design and Development Assessment

  • Sufficient understanding and analysis of the business needs with the business requirements clearly playback to business stakeholders and gained their confirmation
  • Design of the solution sufficiently addresses the problem statement and reflects business and practical realities
  • Employed a human-centered approach to design and develop the integrated AI solution, taking into account ease of use, human-in-the-loop processes, and change management
  • All stages in the model development process are sound in terms of quality and efficiency:
  • Data pipeline engineering and data model design
  • Model integration
  • Test planning
  • Able to put in controls to track and manage input data quality
  • Able to synthesize and integrate technologies for AI solution implementation, testing and deployment in production with knowledge of the trade-offs
  • Management and security plan for project data defined and implemented
  • Able to clarify the costs or the budget for ML systems such as costs of data collection and data processing
  • Able to clarify and reason about scaling issues of ML systems
  • Able to plan for change in the tech stack of ML systems
  • Able to set up infra-as-code and automation to support ML development and deployment workflows
  • Able to provide inputs into the procurement of ML infrastructure
  • Able to interpret the output and the anomalies of ML systems for business leaders’ appreciation
  • Able to propose relevant metrics and track system performance of ML deployment
  • Able to demonstrate good understanding of AI technical competencies, as exampled below, for engineering high-performance AI solution:
  • Computational modelling, computer vision, text analytics with computational thinking skills
  • Self-learning systems

3. AI Governance Assessment

  • Able to explain the consequences of flawed data and model bias
  • Able to explain the steps taken to reduce model bias during solution development
  • Include effort to perform assessment on the fairness of the ML models to mitigate data bias
  • Regulations to ensure that the cybersecurity and safety of users and systems are taken into account during the full lifecycle of an AI product

4. Leadership Assessment

  • Provide a clear plan and evidences of successful execution for coordinating and managing a development team during the project
  • Provide example of conflict resolution
  • Provide a clear plan and evidences of successful execution on managing and planning resources and budgets within his or her team

5. Communication [During Interview]

  • Able to elaborate the business objectives or goals of the project
  • Able to explain technical design and implementation of the project well to non-technical person
  • Able to summarize the benefits and limitations of the AI approach to solving business challenges

Technical Report Template

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Application fee for AI Engineer Level 2