As I delve into the world of AI engineering, I’ve come across a fascinating trend – companies are shifting their focus from traditional machine learning system design to AI engineering, encompassing a broader range of technologies like IR, RAG, agents, LLMs, chatbots, and assistants. This paradigm shift has left many of us wondering how to prepare for interviews that focus on this specific area of AI engineering.
I recently stumbled upon a Reddit post from someone preparing for an ML system design interview at a large, established company. The twist? The interview would focus on AI engineering, and the candidate was struggling to find resources to prepare.
It’s not surprising, given that most resources available online cater to traditional ML system design prep. But what does it take to succeed in an AI engineering interview? How can we approach these new-ish technologies and ace the interview?
In this post, we’ll explore the differences between traditional ML system design and AI engineering, and provide some valuable insights on how to prepare for an AI engineering interview.
Whether you’re a seasoned professional or just starting out in the AI field, this post aims to offer a comprehensive overview of what to expect and how to tackle the challenges of an AI engineering interview.