Career Transition Lab

About this course

A mentored lab for learners changing roles: from software, data or adjacent fields into AI, ML, generative AI or architecture roles, or from engineer to architect. Learners map target roles to recognised competence profiles, run an honest gap analysis, build a portfolio that earns interviews, rehearse ML, generative-AI, system-design, architecture and behavioural interviews with practising mentors, and leave with a personal learning plan they own.

For: student, software engineer, data professional, architect, ml practitioner.

Learning outcomes

On successful completion you will be able to:

1. 1Explain the main AI, ML and architecture role families, the competences each requires (mapped to e-CF and ESCO profiles), and how hiring for them works.
2. 2Analyse one's own skills against a target role and produce a gap analysis and a personal learning plan with measurable milestones.
3. 3Build a portfolio of at least two evidence pieces (project write-ups, design documents or code) that demonstrates competence for the target role.
4. 4Perform effectively in technical interviews for ML, generative-AI, system-design and architecture roles, structuring answers around requirements and trade-offs.
5. 5Answer behavioural interview questions with structured evidence and negotiate an offer with a clear understanding of its components.
6. 6Take ownership of continuing professional development, reflecting on feedback and adjusting the plan.

Modules and lessons

1. Week 1Positioning your transition⌄

   • The role landscape and your positioning
2. Week 2Building evidence⌄

   • A portfolio that gets interviews
3. Week 3ML and generative-AI interviews⌄

   • ML and generative-AI interviews
4. Week 4System design and architecture interviews⌄

   • System design and architecture interviews
5. Week 5Behavioural interviews and offers⌄

   • Behavioural interviews, negotiation and offers
6. Week 6Owning your development⌄

   • Your learning plan and next steps

Assessment

• Portfolio (40%)

  Gap analysis, personal learning plan and two portfolio pieces reviewed by a mentor.

• Viva (40%)

  Two recorded mock interviews with practising mentors - one technical, one behavioural - scored against a published rubric.

• Peer review (20%)

  Peer review of another learner's role map and learning plan, plus a short reflective note on feedback received.

Pass mark 60%. Grades: Distinction, Merit, Pass.