Interview contents
Interview
Interview preparation is no longer only about algorithms, APIs, and system design templates.
Modern engineering interviews increasingly include AI literacy: what AI systems are, what language models can and cannot do, how AI features change product architecture, and how to reason about reliability, cost, latency, safety, and evaluation.
This section combines Kavriq’s current AI interview tracks with its software-engineering preparation library. The imported material is preserved as v1 so it can be improved without breaking stable links.
AI Literacy
AI literacy questions may appear as a standalone interview round, but they may also show up inside system design, knowledge, product judgment, or bar-raiser style conversations.
The goal is not to memorize buzzwords. The goal is to explain AI concepts clearly enough to make engineering trade-offs.
AI Engineer Prep
This path collects broad interview preparation for AI Engineer roles: ML fundamentals, deep learning, LLMs, agents, RAG, AI system design, infrastructure, coding patterns, and behavioral judgment.
Frontier AI Engineering Prep
This path is for engineers targeting frontier AI labs such as Anthropic, OpenAI, and xAI. It is narrower than general AI Engineer prep.
It covers three role families: Applied AI work on coding agents and evals; Agent Systems work on harnesses, sandboxing, and orchestration; and Research Developer Productivity / AI Infrastructure work on Kubernetes, CI, distributed systems, and research platforms.