What do model-serving frameworks (Triton, TorchServe, vLLM, TGI) provide, and how do you choose?
Almost nobody builds a serving stack by hand. What interviewers watch for is whether you know what frameworks give you (batching, multi-model, GPU scheduling) and why LLM-specific servers even exist when general ones batch already.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Almost nobody builds a serving stack by hand. What interviewers watch for is whether you know what frameworks give you (batching, multi-model, GPU scheduling) and why LLM-specific servers even exist when general ones batch already.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
