Interview library
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Nine focused question banks for AI, ML, cloud, data, and delivery roles. Use them to understand the terrain, then build a role-specific focus plan. AI Engineer also includes scored incident practice.
Forward Deployed Engineer (AI)
Forward Deployed Engineer is the fastest-growing job title in AI. OpenAI, Anthropic, Palantir, and dozens of AI startups hire FDEs to embed with customers and turn models into working systems. The interview tests engineering depth, customer judgment, and speed. These questions come from real FDE loops.
AI Engineer
AI engineer is the title companies use for people who build products on top of LLMs — distinct from ML engineers who train models. Interviews test practical LLM system building: RAG, agents, evals, and cost. These are the questions that show up in real loops.
AI Product Manager
AI product manager interviews test product judgment under probabilistic behavior: choosing a real workflow, defining quality, working through model and data constraints, setting launch metrics, and aligning engineering, research, design, legal, security, go-to-market, and customers. These questions reflect the recurring themes in our current official-board sample of AI product roles.
Cloud Solutions Architect
Solutions architect interviews — AWS, GCP, Azure, or vendor-side — test system design, cost judgment, customer communication, and increasingly AI workload architecture. These questions recur in both cloud-provider and enterprise SA loops.
Data Engineer
Data engineering interviews center on SQL depth, pipeline design, and the modern warehouse stack — with AI-data topics (vector stores, LLM-ready data) appearing in 2026 loops. These questions mirror what shows up in real interviews at both startups and large companies.
DevOps Engineer
DevOps engineer remains one of the most-searched tech roles, and interviews in 2026 increasingly mix classic infrastructure questions with AI-era topics like GPU workloads and LLM-app deployment. These questions cover what interviewers actually ask, with pointers on what a strong answer includes.
MLOps Engineer
MLOps engineer interviews sit between DevOps and ML: you'll be tested on model deployment, monitoring, reproducibility, and increasingly LLMOps. These questions cover the loop most companies actually run.
Machine Learning Engineer
ML engineer interviews still test classical fundamentals — the difference from AI engineer loops is depth on training, data, and deployment of models you own. Expect coding, ML theory, and system design rounds. These questions recur across FAANG and AI-startup loops.
Prompt Engineer / AI Product Engineer
Pure prompt-engineer roles are folding into AI product engineering, but the interview questions persist: companies test systematic prompting, evaluation discipline, and product judgment around LLM behavior. Here's what gets asked.
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Research behind the guides
Our AI Engineer study covers 3,647 postings at 198 companies. Explore the skills data or see posted salary ranges.