From role 2.3 AI Engineer · EQF EQF6
Develop functional AI-enabled software solutions by selecting, applying, and integrating suitable AI models and tools to address moderately complex real-world problems collaboratively
3.1 Select appropriate AI models and software tools for a given application context, such as natural language processing or computer vision tasks.
Assessed by AI Model Selection Report and Written Justification, assessed for appropriateness of selected AI models and software tools for the given application context, such as natural language processing or computer vision tasks.
3.2 Implement AI components and integrate them into software solutions independently, using frameworks like TensorFlow or PyTorch.
Assessed by AI Component Demonstration and Code Submission, assessed for independent implementation and integration of AI components into software solutions using frameworks like TensorFlow or PyTorch.
3.3 Analyse application requirements to adapt AI solutions for real-world scenarios, such as recommendation systems or predictive analytics.
Assessed by Requirements Analysis Document and Use Case Mapping, assessed for analysis of application requirements and adaptation of AI solutions to real-world scenarios such as recommendation systems or predictive analytics.
3.4 Evaluate AI outputs for correctness, efficiency, and user relevance in team-based projects, using performance metrics and user feedback.
Assessed by AI Performance Evaluation Report and Metric Analysis, assessed for evaluation of AI outputs for correctness, efficiency, and user relevance using performance metrics and user feedback.
3.5 Document and communicate technical decisions and results to peers and stakeholders, using reports, diagrams, or presentations.
Assessed by Technical Documentation and Presentation, assessed for clear documentation and communication of technical decisions and results to peers and stakeholders.
3.6 Test and debug AI components within software applications, using unit tests and scenario- based validation.
Assessed by Test Reports, Unit Test Logs, and Debugging Evidence, assessed for testing and debugging of AI components within software applications using unit tests and scenario-based validation.