B.6 Systems Engineering

3.1 Design AI infrastructure solutions, including cloud-based pipelines and model deployment environments, for specific operational scenarios such as predictive analytics or recommendation systems.

Assessed by Evaluation of AI design diagrams and design report submission, assessed for fit between cloud-based pipelines, model deployment environments, and the specified operational scenario.

3.2 Integrate AI components and services with existing IT systems using APIs, microservices, or containerization tools to ensure functional reliability.

Assessed by Review of integrated AI module and integration report, assessed for correct use of APIs, microservices, or containerization tools and evidence of functional reliability within existing IT systems.

3.3 Validate AI system performance and security through structured testing procedures such as unit tests, load testing, or penetration testing.

Assessed by Assessment of AI test report and performance dashboard, reviewed for structured unit, load, or penetration testing evidence covering AI system performance and security.

3.4 Apply ethical AI practices, such as bias detection in datasets, privacy safeguards, and explainable AI techniques, when implementing AI systems.

Assessed by Review of AI bias report, privacy documentation, and explainability report, assessed for demonstrated application of bias detection, privacy safeguards, and explainable AI techniques during implementation.

3.5 Evaluate solutions and propose improvements to AI infrastructure systems within team- based projects using performance metrics and user feedback.

Assessed by Evaluation of AI evaluation report and recommendations report, assessed for use of performance metrics and user feedback to justify proposed improvements to AI infrastructure systems.

3.6 Document AI infrastructure design and implementation processes clearly, using diagrams, code comments, or reports, to support maintainability and knowledge transfer.

Assessed by Review of AI project documentation, assessed for clarity, completeness, and usefulness of diagrams, code comments, or reports for maintainability and knowledge transfer.

3.1 Design AI infrastructure solutions, including cloud-based pipelines and model deployment environments, for specific operational scenarios such as predictive analytics or recommendation systems.

Assessed by Evaluation of AI design diagrams and design report submission, assessed for fit between cloud-based pipelines, model deployment environments, and the specified operational scenario.

3.2 Integrate AI components and services with existing IT systems using APIs, microservices, or containerization tools to ensure functional reliability.

Assessed by Review of integrated AI module and integration report, assessed for correct use of APIs, microservices, or containerization tools and evidence of functional reliability within existing IT systems.

3.3 Validate AI system performance and security through structured testing procedures such as unit tests, load testing, or penetration testing.

Assessed by Assessment of AI test report and performance dashboard, reviewed for structured unit, load, or penetration testing evidence covering AI system performance and security.

3.4 Apply ethical AI practices, such as bias detection in datasets, privacy safeguards, and explainable AI techniques, when implementing AI systems.

Assessed by Review of AI bias report, privacy documentation, and explainability report, assessed for demonstrated application of bias detection, privacy safeguards, and explainable AI techniques during implementation.

3.5 Evaluate solutions and propose improvements to AI infrastructure systems within team- based projects using performance metrics and user feedback.

Assessed by Evaluation of AI evaluation report and recommendations report, assessed for use of performance metrics and user feedback to justify proposed improvements to AI infrastructure systems.

3.6 Document AI infrastructure design and implementation processes clearly, using diagrams, code comments, or reports, to support maintainability and knowledge transfer.

Assessed by Review of AI project documentation, assessed for clarity, completeness, and usefulness of diagrams, code comments, or reports for maintainability and knowledge transfer.

4.1 Design AI infrastructure solutions, including cloud-based pipelines and model deployment environments, for specific operational scenarios such as predictive analytics or recommendation systems.

Assessed by Evaluation of AI design diagrams and design report submission, assessed for fit between cloud-based pipelines, model deployment environments, and the specified operational scenario.

4.2 Integrate AI components and services with existing IT systems using APIs, microservices, or containerization tools to ensure functional reliability.

Assessed by Review of integrated AI module and integration report, assessed for correct use of APIs, microservices, or containerization tools and evidence of functional reliability within existing IT systems.

4.3 Validate AI system performance and security through structured testing procedures such as unit tests, load testing, or penetration testing.

Assessed by Assessment of AI test report and performance dashboard, reviewed for structured unit, load, or penetration testing evidence covering AI system performance and security.

4.4 Apply ethical AI practices, such as bias detection in datasets, privacy safeguards, and explainable AI techniques, when implementing AI systems.

Assessed by Review of AI bias report, privacy documentation, and explainability report, assessed for demonstrated application of bias detection, privacy safeguards, and explainable AI techniques during implementation.

4.5 Evaluate solutions and propose improvements to AI infrastructure systems within team- based projects using performance metrics and user feedback.

Assessed by Evaluation of AI evaluation report and recommendations report, assessed for use of performance metrics and user feedback to justify proposed improvements to AI infrastructure systems.

4.6 Document AI infrastructure design and implementation processes clearly, using diagrams, code comments, or reports, to support maintainability and knowledge transfer.

Assessed by Review of AI project documentation, assessed for clarity, completeness, and usefulness of diagrams, code comments, or reports for maintainability and knowledge transfer.

6.1 Develop and implement AI infrastructure solutions for operational use in organisational contexts, such as automated decision-making systems or AI-driven platforms

Assessed by Review of deployed AI system and deployment documentation, assessed for implementation of AI infrastructure solutions for operational use in organizational contexts such as automated decision-making systems or AI-driven platforms.

6.2 Optimise AI system workflows and resources by applying performance monitoring, load balancing, and cost-efficiency strategies to achieve reliability and efficiency

Assessed by Assessment of AI monitoring dashboard and optimization report, assessed for use of performance monitoring, load balancing, and cost-efficiency strategies to improve reliability and efficiency.

6.3 Coordinate teams and manage resources using leadership, project management, or DevOps practices to ensure successful deployment and integration of AI systems

Assessed by Evaluation of AI team coordination report and meeting logs, assessed for evidence of leadership, project management, or DevOps practices used to coordinate teams and resources for deployment and integration.

6.4 Apply ethical AI practices, security measures, and sustainability principles such as GDPR compliance, energy-efficient model training, and secure cloud practices in operational AI infrastructure

Assessed by Review of AI compliance report and audit trail, assessed for evidence of GDPR compliance, ethical AI practices, security measures, energy-efficient model training, and secure cloud practices in operational AI infrastructure.

6.5 Evaluate operational AI systems and propose improvements for efficiency, regulatory compliance, and team effectiveness using metrics, audits, and workflow analysis

Assessed by Assessment of AI KPI report and recommendation report, assessed for use of metrics, audits, and workflow analysis to evaluate operational AI systems and justify improvements for efficiency, regulatory compliance, and team effectiveness.

6.6 Train and mentor team members in AI infrastructure best practices, tools, and processes to enhance operational capability and knowledge sharing

Assessed by Evaluation of AI training materials and mentorship documentation, assessed for coverage of AI infrastructure best practices, tools, and processes and for evidence of knowledge sharing with team members.

6.7 Monitor and report operational KPIs for AI systems, including uptime, performance, and cost metrics, to inform continuous improvement strategies

Assessed by Assessment of AI continuous improvement plan and monitoring dashboard, assessed for monitoring and reporting of uptime, performance, and cost metrics used to inform continuous improvement strategies.

4.1 Develop and implement AI infrastructure solutions for operational use in organisational contexts, such as automated decision-making systems or AI-driven platforms.

Assessed by Review of deployed AI system and deployment documentation assessed for development and implementation of AI infrastructure solutions for operational use in organizational contexts.

4.2 Optimise AI system workflows and resources by applying performance monitoring, load balancing, and cost-efficiency strategies to achieve reliability and efficiency.

Assessed by Assessment of AI monitoring dashboard and optimization report for performance monitoring, load balancing, cost-efficiency strategies, reliability, and efficiency improvements.

4.3 Coordinate teams and manage resources using leadership, project management, or DevOps practices to ensure successful deployment and integration of AI systems.

Assessed by Evaluation of AI team coordination report and meeting logs assessing team coordination, resource management, and use of leadership, project management, or DevOps practices.

4.4 Apply ethical AI practices, security measures, and sustainability principles such as GDPR compliance, energy-efficient model training, and secure cloud practices in operational AI infrastructure.

Assessed by Review of AI compliance report and audit trail assessing ethical AI practices, security measures, sustainability principles, GDPR compliance, energy-efficient model training, and secure cloud practices.

4.5 Evaluate operational AI systems and propose improvements for efficiency, regulatory compliance, and team effectiveness using metrics, audits, and workflow analysis.

Assessed by Assessment of AI KPI report and recommendation report evaluating operational AI systems and proposed improvements for efficiency, regulatory compliance, and team effectiveness.

4.6 Train and mentor team members in AI infrastructure best practices, tools, and processes to enhance operational capability and knowledge sharing.

Assessed by Evaluation of AI training materials and mentorship documentation assessing training and mentoring on AI infrastructure best practices, tools, and processes.

4.7 Monitor and report operational KPIs for AI systems, including uptime, performance, and cost metrics, to inform continuous improvement strategies.

Assessed by Assessment of AI continuous improvement plan and monitoring dashboard for operational KPI reporting, including uptime, performance, and cost metrics, and their use in continuous improvement.