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 production
- how to design production RAG systems
- how to choose between simple RAG, Hybrid RAG, GraphRAG and AgentRAG
- how to build reliable AI agents and multi-agent workflows
- how MCP, tool calling, supervisor agents and router agents fit into production systems
- how contextual memory works in enterprise AI systems
- how to evaluate RAG and agentic AI systems
- how to design guardrails and reduce hallucination
- how to defend against prompt injection and jailbreaking
- how to handle PII, authorisation and secure data access
- how to monitor hallucination, quality, cost and latency in production
- how to review Python AI code for production risks
- how to answer scenario-based AI architecture questions
