33What's your experience architecting LLM-based applications?▼mediumCohereOpenAI1 replies◆ premiumThe experience probe that opens half of all applied-AI loops, and the place resumes go to die. The four-beat walkthrough structure interviewers can actually score, plus the two omissions that quietly downgrade you from architect to API caller.Open full answer →
34How did you ensure the LLM didn't answer outside its designed scope?▼mediumSierraCresta1 replies◆ premiumPast tense matters: the interviewer is asking what you actually enforced at runtime, not what you'd design on a whiteboard. The enforcement chain (gate, starve, screen, measure) with the scope-violation metric that proves it worked.Open full answer →
02What are your day-to-day responsibilities as an MLOps engineer?▼easy★ EssentialAmazonMicrosoftJPMorgan2 repliesunlockedAn experience probe that sinks more candidates than any design question, because a vague answer ends the interview early. The four-plane structure that proves you're an operator, not a data scientist with MLOps on the resume.Open full answer →
07When you say you 'push staging to production,' what does that actually mean for a model?▼easyAmazonDatabricksMicrosoft1 repliesunlockedA real interview question designed to catch resume inflation: candidates say 'we promote to production' and can't name the API call. What actually moves when a model is promoted, and the answer that proves you've done it.Open full answer →
22Do you have experience deploying ML models on Kubernetes for inference? Walk me through the process and your role.▼mediumMicrosoftNVIDIAUber1 replies◆ premiumAn experience probe with a depth gauge: 'we used Kubernetes' fails, and so does reciting the KServe README. The stack-process-role-warstory structure, the resource and probe details that prove hands-on work, and where KServe earns its complexity.Open full answer →
26Have you hit issues scaling an API gateway in front of model inference? What were they and how did you fix them?▼hardAmazonMicrosoftUber1 replies◆ premiumA real customer-interview question that rewards scar tissue: the 29-second timeout wall, retry storms that triple your own load, and connection pools sized for web traffic meeting 30-second inferences. The issues worth claiming and the fixes that prove you were there.Open full answer →
28How have you implemented CI/CD pipelines for ML models? Tools, process, and the hardest problems.▼medium★ EssentialAmazonMicrosoftJPMorgan1 replies◆ premiumThe experience probe where tool soup fails and one well-told pipeline wins. The context-stack-hardest-problem-outcome structure, the two challenges every real implementation hits, and the before/after numbers that make the story land.Open full answer →
29Describe your MLOps environment on AWS. What did you choose for model hosting, and why?▼hardAmazonCapital OneJPMorgan1 replies◆ premiumThe 'why' is the question: AWS gives you five ways to host a model, and interviewers want the decision tree plus the trade-off you knowingly accepted. The walkthrough structure, the real cost math between SageMaker endpoints and EKS, and the answer shape that survives drilling.Open full answer →
07Have you worked on machine learning GPU workloads?▼easyNVIDIACoreWeaveTogether AI1 repliesunlockedSounds like small talk; it's actually a depth probe with follow-ups pre-loaded. A scoping framework that works whether you've run 4 GPUs or 4,000, and the bluff that ends interviews.Open full answer →
18What experience do you have with ML infrastructure, including distributed GPU clusters?▼easyCoreWeavexAIOpenAI1 replies○ sign inThe resume-walk question for infra roles, with a hidden rubric: layer ownership, scale fluency, and one defensible war story. How to answer credibly at any actual scale, and the inflation tells interviewers catch.Open full answer →