What is asked in an AI Architect interview?
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- Name
- Amit Shekhar
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An AI Architect interview tests whether we can design AI systems for a whole company, make model and data decisions with clear trade-offs, review risk, and explain our choices to both engineers and leaders.
In this blog, we will learn about what is asked in an AI Architect interview. We will also see the rounds in the order they usually come, what each round tests, sample questions with short model answers, and the common mistakes candidates make in each round.
We will cover the following:
- What does an AI Architect interview test?
- Round 1: Recruiter screen
- Round 2: AI system design
- Round 3: Model and data decisions
- Round 4: Governance and risk
- Round 5: Prototype or review
- Round 6: Leadership and communication
- Common mistakes
I am Amit Shekhar, Founder @ Outcome School, I have taught and mentored many developers, and their efforts landed them high-paying tech jobs, helped many tech companies in solving their unique problems, and created many open-source libraries being used by top companies. I am passionate about sharing knowledge through open-source, blogs, and videos.
I teach AI and Machine Learning at Outcome School.
Let's get started.
What does an AI Architect interview test?
An AI Architect is a senior role. The person in this role owns the overall design of AI systems across a company: which models are used, how data flows, how security and governance are handled, how cost is controlled, and how everything fits with the existing products.
So, the interview does not test whether we can write a lot of code. It tests whether we can make good decisions when many teams, many products, and real cost are involved.
In every round, the interviewer watches how we reason. We must explain the trade-off behind each choice, say what we give up, and say how we would know if the choice was wrong.
The rounds below come in roughly this order.
Round 1: Recruiter screen
This is a short call, usually 20 to 30 minutes. The recruiter wants to know three things: have we designed AI systems that went to production, have we worked across more than one team, and can we talk about our work in plain language.
Sample question: "Tell me about an AI system you designed that is running in production."
Model answer: We pick one system, say what it does, how many teams depend on it, and name one hard decision we made. For example: "I designed the document search system for the support team. It serves two products, and the hardest decision was to use retrieval over fine-tuning, because the documents change every week."
The recruiter is judging clarity, not depth.
Round 2: AI system design
This is the most important round. It is usually 60 minutes with a senior engineer or an existing architect.
The interviewer gives a problem at company scale, not at feature scale. A common one is: "Design an AI assistant that works across several of our products." The interviewer expects us to find the hard parts on our own:
- Shared data rules: each product has its own data, and some of it cannot be shown to users of another product. The assistant must respect this in every answer.
- An evaluation standard: every team will build on this assistant, so we need one shared way to measure if an answer is good.
- Cost control: one assistant across many products can become the largest cost in the company. We need limits, caching, and cheaper models for simple requests.
- Fallbacks: when the model provider is down or slow, the assistant must still answer, for example by using a smaller backup model or showing a plain search result.
Sample question: "Where do you start?"
Model answer: We start by asking questions. Which products are in scope first? Who owns the data in each product? What is the monthly budget? We write the answers down before we draw anything. Then, we draw the system in layers: a gateway that every product calls, a data access layer that checks permissions before retrieval, a model layer with a primary and a fallback model, and an evaluation layer that scores a sample of answers daily.
Sample question: "Why one shared gateway and not one assistant per product?"
Model answer: One gateway gives us one place for cost limits, one place for logging, and one place to swap the model. The trade-off is that the gateway becomes a single point of failure and a shared bottleneck, so we must design it for high availability and give each product its own rate limit.
The interviewer does not want a single correct answer. The interviewer wants to see the trade-offs and a written decision. A strong candidate ends the round with a short decision record: what we chose, what we rejected, and why.
A quick note for you
No matter which tech domain you work in, get familiar with these topics:
- LLM
- RAG
- MCP
- Agent
- Fine-tuning
- Quantization
We put it all together in one video:
AI Engineering Explained: LLM, RAG, MCP, Agent, Fine-Tuning, and Quantization
No need to stop reading - bookmark it and watch later when you get time. Future you will thank you.
Now, let's get back to the topic.
Round 3: Model and data decisions
Now, let's go deeper into the choices that an AI Architect makes regularly. This round is usually 45 to 60 minutes.
Sample question: "Should we call a model through an API or host the model ourselves?"
Model answer: We compare on four points: data sensitivity, cost at our expected volume, the skills of the team, and how soon we need the newest models. An API is faster to start. Self-hosting gives us control over the data and a lower cost per request at very high volume, but it needs a serving team. We recommend starting with an API, with a rule that no customer data crosses the boundary unless the contract allows it, and a review when the monthly volume crosses a set threshold.
Sample question: "When would you fine-tune, and when would you use RAG?"
Model answer: We use RAG when the knowledge changes often or must be traced back to a source, because the model reads the latest documents at request time. We use fine-tuning when the problem is about style, format, or a narrow task that does not change. Most company problems are knowledge problems, so RAG comes first, and fine-tuning is added only if evaluation shows a gap that retrieval cannot fix.
Sample question: "Where is customer data allowed to go?"
Model answer: We define three zones: data that never leaves our servers, data that can go to an approved provider under a contract, and data that is public. Every AI feature must declare which zone its inputs belong to before it is approved, and the gateway enforces it.
Sample question: "A model provider is retiring the version we use. How do you handle the change across teams?"
Model answer: We run the shared evaluation standard against the new version for every team's use case. Teams whose scores stay the same or improve are switched first. Teams whose scores drop get a fixed period to adjust their prompts, with the old version pinned until then. The switch happens in the gateway, so no team changes its own code.
In this round, the interviewer checks that every answer has a reason and a limit. "It depends" is not an answer. "It depends on these three things, and here is how we decide" is an answer.
Want to become an AI Architect?
These are the skills this role needs, and we teach them in our AI and Machine Learning Program at Outcome School:
- RAG
- AI Agent
- Orchestration and Routing
- AI Security
- Cloud vs On-device Deployment
- Design a RAG System (Chat with Your Documents)
We take you from the fundamentals to building real AI systems, step by step.
Round 4: Governance and risk
This round tests whether we can keep the company safe without blocking every new idea. It is usually 45 minutes, often with someone from security or legal.
Sample question: "A team wants to ship a new AI feature next week. How do you review it?"
Model answer: We use a short checklist. What data goes into the model, and which zone is it in? What does the model output, and who sees it? Can the output cause an action, such as sending an email, without a human check? How is it evaluated, and what is the fallback when it fails? What is the expected monthly cost? If the team can answer all of these in writing, the review takes one meeting.
Sample question: "What can go wrong with an AI feature after it ships?"
Model answer: The model can make up facts that look true. A prompt can leak private data from one user to another. A user can type instructions that make the model ignore its rules. The cost can grow much faster than usage. For each one we name the control that catches it: evaluation, data zones, input filtering, and cost alerts.
Sample question: "How do you say no to a team?"
Model answer: We say no with reasons and with a path forward. For example: "This feature sends customer records to an external provider, and our contract does not cover that. If we move the retrieval step inside our own servers and send only the question, we can approve it." A no without a path forward makes teams hide their work from us.
Round 5: Prototype or review
This round is practical. It is usually 60 to 90 minutes, and it comes in one of two forms.
Form 1: Review. The interviewer gives us a design document or the output of a coding agent and asks us to find the risks. For example, the coding agent has built a connector that pulls customer tickets into a retrieval system. We read it and point out that the connector copies personal fields from each ticket, that there is no check on which product the ticket belongs to, and that there is no test for an empty result.
The interviewer is checking that we review for the right things: data exposure, missing permission checks, missing fallbacks, and missing tests. We are not expected to rewrite the code, only to direct the coding agent on what to fix and verify the fix.
Form 2: Prototype. The interviewer gives us one assumption to test, such as "a smaller model is good enough for ticket classification." We work with a coding agent to build a quick prototype: we describe the task clearly, let the agent write the script that runs both models over a sample of tickets, review what it produced, and run it.
The method matters more than the result. The interviewer wants to see that we pick a fair sample, measure the same thing for both models, and state what the prototype does not prove.
Sample question: "Why did you test on only 200 tickets?"
Model answer: We say that 200 tickets is enough to see a large gap between the models and not enough to see a small one. If the gap is small, we recommend a bigger test before any decision.
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Round 6: Leadership and communication
This round is usually with a director or a head of engineering. The interviewer is checking whether we can get people to agree on a decision, not only draw it.
Sample question: "Explain to a non-technical leader why we are spending more on the model than on the servers."
Model answer: We avoid every technical term. For example: "Each answer the assistant gives costs us a small amount, like a phone call. We make many calls per day, so the bill is large. We can lower it by answering easy questions with a cheaper option and keeping the expensive option for hard ones, and we will check that the answers stay good."
Sample question: "Two teams disagree on which model to use. How do you handle it?"
Model answer: We do not pick a side in the meeting. We ask both teams to run the same evaluation set on both models and bring the numbers. If the numbers are close, we choose the model that is cheaper to operate across the company. We write the decision down with the numbers, so that both teams can see it was decided on evidence.
Sample question: "How do you mentor engineers who want to become architects?"
Model answer: We give them one real decision to own, with a small scope and a real cost. We ask them to write the decision record and defend it. We review the record, not the code, and we widen the scope over time.
Common mistakes
Most of the time, we do mistakes in this interview for a few reasons.
- Designing a feature instead of a system. Candidates design one assistant in detail and forget the shared data rules, the evaluation standard, and the cost plan. The interviewer asked about a company, not a product.
- Giving one answer with no trade-off. Saying "we must use RAG" is weak. Saying "we must use RAG because the documents change weekly, and we accept slower answers for that" is strong.
- Never writing the decision down. An architect who keeps every decision in conversation leaves no record for the teams that must follow it.
- Reviewing for style instead of risk. Candidates comment on variable names and miss the connector that leaks personal data.
- Treating the coding agent as the final step. In the prototype round, candidates run what the agent produced without reading it. The review and the verification are the architect's job.
Now we must have understood what is asked in an AI Architect interview, what each round tests, and how to prepare an answer with a clear trade-off for each one.
Frequently Asked Questions
Do we need to write a lot of code in an AI Architect interview?
No. The AI Architect interview does not test whether we can write a lot of code. It tests whether we can make good decisions when many teams, many products, and real cost are involved. Even in the prototype round, we work with a coding agent, review what it produced, and verify it.
Which is the most important round in an AI Architect interview?
The AI system design round is the most important round. It is usually 60 minutes with a senior engineer or an existing architect. The interviewer gives a problem at company scale and expects us to find the hard parts on our own, such as shared data rules, an evaluation standard, cost control, and fallbacks.
Is there a single correct answer in an AI Architect interview?
No. The interviewer does not want a single correct answer. The interviewer wants to see the trade-offs behind each choice and a written decision. "It depends" is not an answer, but "It depends on these three things, and here is how we decide" is an answer.
What is a decision record in an AI Architect interview?
A decision record is a short written note of what we chose, what we rejected, and why. A strong candidate ends the system design round with one. An architect who keeps every decision in conversation leaves no record for the teams that must follow it.
That's it for now.
Thanks
Amit Shekhar
Founder @ Outcome School
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