Senior
LLM Integration Interview Questions for .NET Developers
Senior .NET interview questions on Microsoft.Extensions.AI, streaming, function calling, structured output, provider abstraction, retries and testing LLM code.
AI Engineering with .NET
AI engineering interview questions for .NET developers: LLM integration, RAG and vector search, AI agents and MCP, production readiness, ML.NET and ONNX.
5 interview guides · 50 questions with detailed answers
AI features are now part of many senior .NET roles, and interviewers are adding AI engineering questions to their loops. These pages cover integrating large language models with Microsoft.Extensions.AI, designing RAG systems, and building agents with tools and MCP.
They also cover evaluating and operating AI in production and where classic machine learning with ML.NET still fits.
Architect
RAG and Vector Search Interview Questions
Architect-level interview questions on RAG: chunking, embedding choice, hybrid search, reranking, groundedness, stale data, permissions and cost control.
Architect
AI Agents and MCP Interview Questions
Architect-level interview questions on AI agents and MCP: the agent loop, tool design, orchestration, memory, human-in-the-loop and failure modes in .NET.
Architect
Productionizing AI Applications Interview Questions
Architect-level interview questions on productionizing AI: evaluation, OpenTelemetry GenAI observability, cost, latency, prompt injection and compliance.
Senior
ML.NET and ONNX Interview Questions
Senior .NET interview questions on ML.NET and ONNX: pipeline design, feature engineering, evaluation metrics, PredictionEnginePool deployment and model drift.
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