HackTheRounds Interview Experiences

Microsoft AI Engineer Interview Experience (2026) - Merge Intervals, LCA Tree Switch, LLM Recommendation System Design, Offer

Microsoft Azure AI Engineer loop: Codility OA with Responsible AI MCQs, three coding rounds covering Merge Intervals and LCA, LLM centric system design for spor

By Anonymous · 2026-04-02

Background

The Azure AI org has been hiring AI Engineers aggressively through 2026 and the loop looks different enough from the standard Microsoft SDE process that it deserves its own writeup. I was targeting a Copilot-adjacent team that works on LLM application plumbing and MLOps. My background is three years of backend engineering plus a year doing RAG systems at a smaller enterprise AI shop. I applied through a referral in late February and had an offer five weeks later.

Timeline

Week 0: Referral submitted, recruiter reached out in three days
Week 1: Recruiter phone call, resume walkthrough
Week 2: Online Assessment on Codility, 90 minutes
Week 3: Technical loop, three rounds on the same day
Week 4: Behavioral and hiring manager rounds
Week 5: Offer

Total: about 5 weeks.

Recruiter Screen (30 min)

The recruiter ran a standard background check plus a handful of questions that were obviously designed to weed out candidates without concrete LLM experience. What are you working on right now, what does your RAG stack look like, have you ever deployed a production model behind Azure. Nothing technical deep, but you need specific project names and specific tool names, not generalities. I talked through a RAG pipeline I had built that cut hallucination rate meaningfully using Azure OpenAI plus a vector store, and that was enough to move forward.

Online Assessment (90 min)

Codility, 2 to 3 problems. Mine was two coding problems plus a handful of multiple-choice on Generative AI and Responsible AI. Coding problems were standard medium difficulty (one array and one string manipulation, nothing LLM-flavored). Multiple-choice covered prompt engineering basics, retrieval-augmented generation fundamentals, Azure AI service knowledge (Azure ML, Azure OpenAI, Cognitive Services), and bias and fairness detection. If you have never used the Azure stack the multiple-choice will wreck you because the options are all plausible-sounding.

Technical Onsite (3 rounds)

Round 1: Coding — Merge Intervals

Problem: Given a list of intervals, merge all overlapping ones and return the consolidated list.

Classic sort-by-start-then-sweep. I wrote it in Python with a lambda sort key and a single-pass merge. Runtime O(N log N), space O(N) for the output. The interviewer cared a lot about edge cases: empty input, single interval, intervals that touch but do not overlap, intervals that completely contain one another. I named each explicitly before writing the code and handled them in the loop.

The follow-up was what changes if intervals stream in one at a time and you need the merged set after each insertion. I walked through a sorted container (red-black tree) supporting O(log N) insert with amortized O(1) merge on neighbors. Did not code it but the interviewer was satisfied with the verbal description.

Round 2: Coding — LCA in Binary Tree

Problem: Find the lowest common ancestor of two nodes in a BST first, then generalize to a plain binary tree.

BST version is direct: walk from root, if both targets are less than current go left, if both greater go right, otherwise current node is the LCA. O(log N) expected.

Then the interviewer pulled the switch: remove the BST property, and now it is an arbitrary binary tree. The approach changes fundamentally. Recurse into both subtrees, return non-null if you found a target below, and the first node where both left and right returns are non-null is the LCA. O(N).

The follow-up took the form of a tree modification: reverse every odd-indexed level of the tree. The level-order BFS that swaps values pairwise at odd depths is the clean way, and the interviewer liked that I kept the structure untouched and only swapped values.

is the exact follow-up and I was glad I had drilled it the week before. The BST warm-up at the top of the round is the same muscle as , where the BST ordering lets you short-circuit the search.

Round 3: System Design — Local Sports Recommendation with LLMs

Problem: Design a recommendation service for local sports events that uses an LLM for natural-language query understanding and personalized ranking.

This was the longest and most interesting round. I walked through the whole architecture: an embedding-based retrieval layer for candidate generation, an LLM reranker for top-K ranking with personalization context, and a cold-path evaluation service for offline metric computation.

The interviewer pushed hard on RAG versus fine-tuning. My take: RAG for the long-tail factual content about events because you can update it hourly, fine-tune only if you have clear format or tone requirements that no amount of prompting can lock in. He wanted me to justify both directions and I did.

Then latency. Serving a 70B model in real-time is not going to meet a sub-second SLA, so we talked about quantization (int8 or 4-bit), distillation into a smaller reranker for the hot path, and caching query-result pairs for popular queries. The hard constraint was a sub-300ms p95 for the full pipeline.

Then hallucination. I walked through a post-retrieval validation layer: every event the LLM mentions must appear in the retrieved candidate set, and any that does not gets filtered out. Plus an adversarial red-teaming job that runs nightly on a fixed prompt set to catch regressions. Plus clear guardrails around what the system is allowed to claim (event times, prices, availability). The interviewer was specifically looking for a discussion of the effect-versus-cost tradeoff, not a single "correct" answer.

is not a direct topic match but the general pattern of designing a stateful service with tight SLAs comes from the same muscle.

Behavioral Round (45 min)

Standard Microsoft behavioral stuff, but one question was specifically AI-engineer-flavored: what do you think about the current LLM hype and where do you draw the line between real capability and marketing. I gave a concrete answer naming two places where LLMs are production-ready (structured information extraction, code completion) and two where I have seen failures (medical advice, multi-step reasoning at scale). The other questions were the usual: failure story, cross-team conflict, a project you are proud of. STAR format throughout.

Hiring Manager Round (45 min)

No coding. Strategic discussion about my understanding of Microsoft's AI direction and what I would bring to the team. The HM specifically wanted to know how I thought about cost versus quality when deploying LLMs to consumers. I walked through a real example from my current job where we had reduced inference cost by 60% through a combination of prompt compression, caching, and a smaller distilled model for easy queries, with a quality holdback on the remaining 10%. That concrete number carried the round.

Result

Offer came about a week after the HM round. Microsoft pays competitive comp on the AI Engineer track and the sign-on was meaningful. I negotiated a modest bump after showing a competing offer and accepted.

Tips

Brush up on Azure-specific AI services. Azure ML, Azure OpenAI Service, Cognitive Services, Bot Service. The OA has multiple-choice questions that are trivial if you have used the tools and impossible if you have not.
Always be ready for the BST-to-tree switch. LCA is the canonical example but the pattern repeats: they give you a structured version first, then strip the structure and ask for the general solution.
For LLM system design, lead with tradeoffs, not answers. RAG versus fine-tune, latency versus quality, cost versus freshness. Interviewers want to hear the decision tree, not a single verdict.
Responsible AI is part of the rubric. Hallucination mitigation, bias detection, and safety evaluation come up at every stage. Have one concrete example of how you addressed each in past work.
Quantify everything in the HM round. "Reduced latency" is weak; "reduced p95 from 800ms to 320ms by switching to int8 quantization on the reranker" is strong. Microsoft's HM interviews reward concrete numbers.
Know one real production RAG architecture cold. If you cannot draw an embedding-retrieval-reranking pipeline on a whiteboard in two minutes, you are not ready for this loop.