AI Engineering Roles: What They Do & How to Train for Them
Updated on December 07, 2025 12 minutes read
Frequently Asked Questions
What is an AI engineering role?
An AI engineering role focuses on turning powerful models into working products. That includes wiring models to data and APIs, designing prompts and workflows, adding guardrails, and monitoring performance and safety once systems are live.
How is an AI engineer different from a data scientist or machine learning engineer?
Data scientists usually focus on analysis, dashboards, and insights from data. Machine learning engineers often focus on training, optimising, and deploying custom models.
AI engineers focus on integrating models into products using techniques like RAG, prompt design, agents, and full-stack development. The roles overlap, but AI engineering is generally more product- and integration-oriented.
Which AI engineering role is best for beginners?
If you’re starting out, two accessible options are AI Application Engineer and RAG Developer. Application roles suit people coming from web development, while RAG roles suit those who enjoy data and backend work.
Both paths give you clear project ideas and skills that can be learned in months
What kind of projects should I include in my AI engineering portfolio?
Choose small, focused projects that solve specific problems: a RAG-powered Q&A bot, a support ticket summariser, a document ingestion pipeline, or a workflow agent. Each project should have a clear README, explanation of your design choices, and screenshots or a short demo video.
If you’re in a bootcamp, try to align your capstone with your target role and polish it more than the others. Quality and clarity matter more than sheer quantity.