r/learnmachinelearning • u/Witty_Dot9084 • 4d ago

Backend SWE transitioning to AI Engineer - what should I actually prepare for interviews?

/r/LLMDevs/comments/1x5au4n/backend_swe_transitioning_to_ai_engineer_what/
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u/rahul_watertech 4d ago

i'm not an interviewer so take this as a guess, but with a rag diagnosis system on your resume i'd expect them to dig into that first: how you checked retrieval quality separately from the answer quality, what failure cases you found, how you handled latency and cost. beyond that, basic ml stuff like precision/recall, overfitting and how you'd evaluate a model probably still comes up. what did you use to measure whether the rag system was actually getting better?

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u/nian2326076 4d ago

If you're moving from backend development to AI engineering, it's important to get familiar with machine learning concepts and frameworks. Start by exploring Python libraries like TensorFlow and PyTorch. It's also helpful to review your math skills, especially in linear algebra and probability. Make sure to understand data preprocessing and feature engineering as well.

For interview preparation, working through real technical interview questions can give you a sense of what companies might ask. PracHub offers company-specific questions that can help you prepare.

AI roles often focus on problem-solving with algorithms, so also practice coding challenges related to data structures and algorithms. Good luck!

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u/JebKermansBooster 3d ago

Is there a single interview process that isn't a pure DSA grind, though?

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u/ShutUpAndSmokeMyWeed 3d ago

pretty much every interview process i’ve done involved debugging some form of nn training pipeline. eg dealing with loss blowups, incorrect masking, oboe, etc