AI Engineering Interview Masterclass

About this course

This course contains the use of artificial intelligence.

AI Engineering interviews have changed.

Companies are no longer just asking, “What is RAG?” or “How do you call an LLM API?” They want to know whether you can design, explain, secure, evaluate and improve production-grade AI systems.

This course is a practical interview preparation masterclass for software engineers, AI engineers, senior engineers and platform engineers preparing for modern AI Engineering interviews.

You will learn how to answer real interview questions around RAG, Advanced RAG, GraphRAG, AgentRAG, AI agents, multi-agent systems, contextual memory, evals, guardrails, observability, prompt injection, PII, access control, latency, cost optimisation and production architecture.

The focus is not on memorising definitions. The focus is on learning how to structure strong interview answers that show practical engineering judgement.

You will learn how to explain:how to take an AI proof of concept into productionhow to design production RAG systemshow to choose between simple RAG, Hybrid RAG, GraphRAG and AgentRAGhow to build reliable AI agents and multi-agent workflowshow MCP, tool calling, supervisor agents and router agents fit into production systemshow contextual memory works in enterprise AI systemshow to evaluate RAG and agentic AI systemshow to design guardrails and reduce hallucinationhow to defend against prompt injection and jailbreakinghow to handle PII, authorisation and secure data accesshow to monitor hallucination, quality, cost and latency in productionhow to review Python AI code for production riskshow to answer scenario-based AI architecture questions

Every major topic is taught from an interview perspective.

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