Managing & Collaborating
As an AI engineer you rarely build alone: you plan and track your own work so it's visible to the team, spot process bottlenecks before they cost a sprint, and make sure what you hand over — code, data, or a decision — is usable by someone other than you.
Starting Points
- Data Collection Plan — Business and Data Understanding
- Initial Pitch — Business and Data Understanding
- Requirement Specifications — Business and Data Understanding
- Experiment Tracking Logs — Machine Learning Model Engineering
- Final Project Presentation — Deployment and Maintenance
Level 2 Learning Outcome Managing & Collaborating
You monitor the process and the progress of collaboration, identify bottlenecks, make these discussable and actively contribute to the joint result. Success criteria:
- You plan and organise the work according to an agreed process
- You actively review progress and collaboration and draw conclusions about what is going well and what can be improved
- You address team members constructively when you identify bottlenecks
- You ensure that your work is transparent and usable for others
- You take on tasks that contribute to the team goal, even if they fall outside your own task package
Level 3 Learning Outcome Managing & Collaborating
You anticipate bottlenecks in the process and collaboration, intervene proactively, take responsibility for the team result and support others in their contribution to the joint goal. Success criteria:
- You anticipate risks and bottlenecks in the process before they become problems and ensure that the work remains transparent and transferable
- You critically evaluate progress and collaboration, recognise patterns and proactively adjust the process where needed
- You address team members constructively about behaviour and contribution, even in complex or tense situations
- You ensure that your work aligns with that of others and contributes to the joint result
- You support others in their contribution to the team goal