How to Showcase Your AI Experience in Behavioral Interviews
Embed your AI experience in your Interview Stories
Hey, Prasad here 👋 I’m the voice behind the weekly newsletter “Big Tech Careers.”
This week, I’m sharing how you can showcase your AI Experience when applying for AI roles at tech roles!
I’m also running a full-day live workshop on Interview Prep on May 2nd, where I cover all the strategies to clear your big tech interviews and demonstrate how to use this custom AI tool in practice.
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Getting a AI role at a top tech company means sitting in front of a hiring manager and answering questions about your AI experience. Many candidates struggle here — they’ve done real work, but they don’t know how to present it in a way that lands.
Here’s what you need to know.
What Interviewers Are Actually Testing
When a hiring manager asks “tell me about an AI project you worked on,” they’re looking for three things:
Hands-on depth. Did you actually build it, or were you just in the room when someone else did? They want to hear specifics — the tools you used, the problems you hit, the decisions you made.
Critical thinking. AI tools produce wrong outputs. Interviewers want to know whether you trust everything a model tells you, or whether you question it and dig deeper.
Learning ability. AI is moving fast. They want to see that you’re keeping up — that you learn from each project and apply that knowledge going forward.
The Five Questions You’ll Likely Face
These come up in interviews related AI roles :
Tell me about the most impactful AI solution you’ve designed or contributed to.
Walk me through a solution that required you to learn something new — and how you identified your knowledge gaps.
Tell me about a time an AI tool gave you a wrong or misleading output.
Describe a time you explained an AI solution to a non-technical stakeholder.
Have you ever flagged a bias or ethical concern in an AI system?
If you can answer these with a real story from your experience if you have, you’re in good shape.
Use the STAR Framework — with an AI Twist
Most people know STAR: Situation, Task, Action, Result. It’s a solid structure for any behavioural interview. When talking about AI work, add few additional details!
Here’s the AI edition:
Situation — What problem existed that AI could solve?
Task — What specific outcome were you trying to achieve with AI?
Action — What tool or approach did you choose, and why that one?
Why AI — Why was AI the right approach here, rather than something else?
Result — What was the measurable outcome?
What I Learned — What did this teach you about working with AI?
That last point matters more than most candidates realise. Interviewers at top companies are specifically listening for self-awareness and growth. Telling them what you’d do differently is a strength.
A Real Example
Here’s how this framework plays out in practice, using a real interview answer for a Data and AI Solutions Engineer role at a Data and AI company .
The question: “Can you talk me through a data and AI solution that you’ve either designed or contributed to recently?”
Situation
Let me talk about a customer which is a global recruitment agency operating across around 40 countries – where I implemented Gen AI solution in Q3/Q4 last year.
They had been doing Gen AI experiments but wanted to move beyond experiments and apply AI in a way that would genuinely improve recruiter productivity across regions — while staying compliant with HR and legal requirements specific to each country.
Recruiters were spending too much time searching across multiple portals and documents. The business wanted something scalable and safe.
There was also a competitive threat from another vendor, who were pitching their Gen AI capabilities heavily, so there was pressure to move quickly.
Task:
As a Principal Solutions Architect, my role was to find the right use cases, shape the architecture, do initial prototypes and to enable the customer teams to take the solution forward.
I ran workshops with business and technical teams to identify high-priority Gen AI use cases. From an initial list of around 8–10, I narrowed to 3 use cases: a recruiter knowledge assistant (RAG-based), candidate matching at scale, and safe/consistent AI deployment. I prototyped 2 of the 3.
Action:
For the RAG-based knowledge assistant, I designed the full ingestion and query pipeline: documents were stored in S3, chunked via AWS Lambda, embedded using Titan embeddings, and stored in OpenSearch as the vector database. The model was Claude on Bedrock.
The assistant was giving arbitrary answers even after everything was set up. I initially looked at the model and prompt engineering, but when I dug into the chunking and retrieval stage specifically, I found the problem. I had set a 512-token chunk size, which was working fine for other use cases but not here.
The HR policies were country-specific, and the token boundary was splitting sentences mid-context — so when a recruiter asked a query, they were not getting accurate answers as the context window was bleeding across boundaries. Switching to sentence-boundary splitting fixed it.
In parallel, I worked on an evaluation and safety framework — this was the bigger learning curve, and it was less about technology and more about navigating internal customer dynamics.
I built 80 gold standard test cases and a confidence threshold scoring system, so the COE team had a clear basis to approve moving to production.
To get buy-in across the organisation, I flew to customer location for 7 days with 4 colleagues. I ran a 5-day workshop with their business and tech teams.
I got senior recruiters to do reverse-shadow sessions on the AI knowledge assistant. The customer legal and compliance teams flew in from Netherlands, Malaysia and Australia to be in the room.
I worked through two specific mandates from customer leadership: quality must improve over time (they understood that solution will not be perfectly accurate at launch), and responses must remain compliant with HR and legal policies across all countries.
For this opportunity, we had top-down buy-in from the CEO and CTO but I also had to bring in line-of-business owners on the ground, country by country to move things forward.
Result:
Recruiter search time reduced by 30–40%, validated through pre and post evaluation criteria.
An unexpected byproduct was that new recruiter ramp-up time dropped by 20% — the goal had been to help existing recruiters, but because there was now one portal instead of five, new joiners had a dramatically simpler onboarding experience.
I’ll give an example of a specific edge case outcome which set the benchmark. In some counties of far east countries, contract rates for specific job roles change monthly and recruiters had to manually check government portals before rolling out offers.
I automated this — new government documents are ingested into S3, chunked, and served through the RAG system with guardrails. This was tested with senior recruiters multiple times and certified.
What Comes After the Main Question
Interviewers don’t stop at one question. Once you’ve told your story, expect follow-ups like:
How did you prioritise which use cases to prototype?
Did this go to production?
Were there alternative architectures you considered?
How would you approach this differently today, given how much the space has moved?
How did you handle conversations with the business about the risks of LLMs processing personal data?
The candidates who answer these well are the ones who were genuinely involved in the work — who made real decisions, hit real problems, and had to explain their reasoning to real stakeholders.
I’ll cover the entire flow in the full day workshop on May 2nd.
IN THIS WORKSHOP YOU’LL LEARN
How to craft Big Tech ready resume
How to deliver high-impact responses in STAR format
How to craft compelling technical achievement stories
How to effectively communicate leadership experiences
How to share lessons learned from past failure and conflict experiences
How to craft and deliver senior level responses to get hired at the right level
Then put it all into practice with FREE AI-powered mock interviews for a week using custom AI tool!
Last chance to get 50% off. Use the code LASTCHANCE50!