Microsoft AI Engineer Case Interview: Designing an Azure-scale Copilot for Microsoft 365

This case simulates a real Microsoft AI engineering discussion where you design, evaluate, and iterate on an enterprise-ready Copilot feature that summarizes Microsoft Teams meetings and generates grounded action items using Microsoft Graph data. You will clarify ambiguous requirements, propose an LLM-centric system on Azure, and reason about Responsible AI, privacy, reliability, and cost at Microsoft’s scale. What the interviewer covers at Microsoft: - Problem framing and customer focus: Translate a VP’s request (“help our 100K-user tenant get meeting summaries with citations from files and emails”) into concrete goals, north-star metrics (e.g., groundedness, hallucination rate, P50/P95 latency, cost/1K users), and success criteria. Expect to create clarity, generate energy, and deliver a pragmatic plan aligned with Microsoft’s values of Respect, Integrity, and Accountability. - Solution approach: Choose between Azure OpenAI Service vs. fine-tuned smaller models on Azure Machine Learning; justify RAG over pure fine-tuning; define chunking, embeddings, and retrieval via Azure AI Search/Graph connectors; design prompts, tools/functions, and citation strategy. Discuss multi-turn orchestration (Azure Functions/Durable Functions) and streaming UX in Teams. - Architecture on Azure: Sketch an end-to-end design using Event Hubs (ingest transcripts), Storage/ADLS, Azure Cognitive Services (STT if needed), Azure AI Search (vectors + filters), model serving (Azure OpenAI or AML endpoints), caching (Redis), secrets (Key Vault), networking (Private Link/VNet), identity (Managed Identity, AAD), monitoring (App Insights/Log Analytics), and rollout via GitHub Actions. - Responsible AI and compliance: Apply Microsoft’s Responsible AI principles—fairness, reliability/safety, privacy/security, inclusiveness, transparency, accountability. Propose guardrails (Azure AI Content Safety, PII redaction, toxicity filters), red-teaming, prompt injection defenses, grounding checks, and human-in-the-loop review for sensitive outputs. Address tenant isolation, data residency, GDPR/HIPAA scenarios, and Microsoft SDL mindset. - Evaluation and MLOps: Define offline and online eval (ROUGE/BLEU for summarization, groundedness scoring, human ratings), canary/A–B tests, drift/quality monitoring, cost controls (token budgeting, prompt compression, output length constraints), rollback plans, model/version governance, and incident response. - Tradeoffs and scalability: Navigate latency vs. quality (rerankers, hybrid search), cost vs. accuracy (smaller models for reranking, larger for final), and build-vs-buy choices. Discuss failure modes, SLOs, and regional rollout. - Collaboration and culture fit: Demonstrate One Microsoft collaboration (partnering with Graph, M365, Security, and Compliance teams), crisp written/verbal communication, and a growth mindset via iterative scoping and risk-based prioritization. Format and expectations: 5–10 minutes clarifying questions; 25–30 minutes system/ML design and diagramming; 10–15 minutes Responsible AI, privacy, and eval; 10–15 minutes deep-dives on tradeoffs and cost; final 5 minutes to summarize decisions, risks, and next steps. The interviewer will probe with real-world constraints (e.g., tenant with strict DLP, noisy transcripts, or regional data boundaries) and expects concrete, Azure-first design choices.