Generative AI System Design Interview Autodesk

Ready to ace your AI/ML, GenAI, or Agentic System Design interview and build the depth expected at senior engineering levels?

As a Principal Software Engineer – AI/ML at Autodesk with 12+ years of industry experience, I’ll help you go beyond textbook ML system design and develop a practical understanding of how modern AI systems are actually designed, integrated, scaled, and operated in production.

During our 1:1 session, we can dive into:

• End-to-end GenAI system design — from user request to retrieval, model orchestration, tool execution, response generation, evaluation, and observability
• RAG architecture in practice — ingestion pipelines, chunking, embeddings, vector databases, hybrid retrieval, reranking, context construction, citations, freshness, and access control
• AI Agents and Agentic workflows — planning, reasoning loops, tool selection, state management, memory, retries, guardrails, and multi-agent coordination
• MCP Servers and tool ecosystems — designing MCP-based integrations, exposing tools and resources, authentication, permissions, context boundaries, and connecting agents to enterprise systems
• Skills and reusable agent capabilities — structuring domain-specific skills, tool abstractions, reusable workflows, orchestration patterns, and capability discovery
• LLM orchestration patterns — routers, planners, executors, reflection, human-in-the-loop workflows, structured outputs, asynchronous jobs, and event-driven architectures
• Production-grade tool calling — API design, idempotency, authorization, rate limits, failure handling, retries, fallbacks, and safe execution
• Conversation state and memory systems — short-term context, long-term memory, retrieval strategies, summarization, personalization, and privacy considerations
• Model selection and routing — choosing between frontier models, smaller models, embedding models, rerankers, and specialized models based on quality, latency, and cost
• Evaluation and observability for GenAI — offline evaluation, LLM-as-a-judge, human evaluation, tracing, prompt/version tracking, retrieval metrics, agent success rates, hallucination monitoring, and production feedback loops
• Security and responsible AI architecture — prompt injection, data leakage, tool abuse, permissions, sandboxing, tenant isolation, PII handling, and enterprise governance
• Scalability, latency, and cost optimization — caching, batching, streaming, context optimization, model routing, token economics, concurrency, and infrastructure trade-offs
• Real-world system design exercises — such as designing an enterprise AI assistant, coding agent, research agent, document intelligence platform, customer-support copilot, multi-agent workflow, or MCP-powered AI platform

We’ll focus heavily on practical architectural decisions and engineering trade-offs: what breaks in production, what looks good on a whiteboard but does not scale, where agents actually add value, when deterministic workflows are better, and how to design systems that are reliable rather than just impressive demos.

I’ll also help you develop a repeatable interview framework for tackling open-ended questions around GenAI, RAG, agents, MCP, and modern AI platforms—covering requirements, architecture, data flow, model choices, APIs, failure modes, evaluation, security, scale, and cost.

Whether you’re targeting a Senior, Staff, Principal, AI Platform, ML Engineer, or GenAI Engineer role, we can tailor the discussion to your experience and the type of companies you’re interviewing with.

Book a session and let’s work through real-world GenAI and Agentic System Design problems together.