# Lessons Learned from Educating AI Engineers

arXiv: https://arxiv.org/abs/2103.10703 · HTML: https://mlsanity.com/paper/2103.10703/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 19 Mar 2021.

## Builds on (most cited first, 2 of 2)

- [Engineering AI Systems: A Research Agenda](https://mlsanity.com/paper/2001.07522/) (2020)
- [Teaching Software Engineering for AI-Enabled Systems](https://mlsanity.com/paper/2001.06691/) (2020)

## Built on by (most cited first, 3 of 3)

- [Teaching MLOps in Higher Education through Project-Based Learning](https://mlsanity.com/paper/2302.01048/) (2023)
- [Perspective of Software Engineering Researchers on Machine Learning Practices Regarding Research, Review, and Education](https://mlsanity.com/paper/2411.19304/) (2024)
- [Scaffolding Collaborative Learning in STEM: A Two-Year Evaluation of a Tool-Integrated Project-Based Methodology](https://mlsanity.com/paper/2509.02355/) (2025)

## Abstract (arXiv)

Over the past three years we have built a practice-oriented, bachelor level, educational programme for software engineers to specialize as AI engineers. The experience with this programme and the practical assignments our students execute in industry has given us valuable insights on the profession of AI engineer. In this paper we discuss our programme and the lessons learned for industry and research.