Full Mock Interview

This lesson simulates a complete 60-minute senior AI engineering interview. Read it actively - pause at each section, form your own answer before reading the model response, and score yourself honestly.

Treat this as a dress rehearsal, not a spectator sport.

Interview Structure

| Phase | Duration | Focus | |-------|----------|-------| | Introduction | 5 min | First impression, background summary | | System Design | 25 min | Technical depth, architecture thinking | | Behavioural | 15 min | Leadership, conflict, growth | | Your Questions | 10 min | Curiosity, judgement, culture fit | | Wrap-Up | 5 min | Closing impression, next steps |

Phase 1: Introduction (5 Minutes)

The interviewer says: "Tell me about yourself and what brought you here."

Your answer should be a two-minute elevator pitch covering:

1. Your current role and one headline achievement.
2. A brief arc of your career - how you got here.
3. Why this specific role and company excite you.

Avoid reciting your CV. Tell a story with a clear thread connecting past, present, and future.

Phase 2: System Design (25 Minutes)

The question: "Design a real-time AI content moderation system for a social media platform with 500 million daily active users."

Step 1: Clarify Requirements (3 min)

Before drawing a single box, ask questions:

• What content types? Text, images, video, or all three?
• What latency target? Must moderation happen before content is visible?
• What is the false positive tolerance? Is it worse to miss harmful content or to wrongly remove safe content?
• What scale? Posts per second, peak traffic patterns?

Assume: Text and images, sub-200ms for text, sub-2s for images, 50,000 posts per second at peak.

Step 2: API Design (3 min)

POST /moderate
  Body: { contentId, contentType, text?, imageUrl?, userId, context }
  Response: { decision: "allow" | "flag" | "block", confidence, categories[] }

Keep it simple. One endpoint, clear contract, extensible with new content types.

Step 3: High-Level Architecture (7 min)

The system has four layers:

1. Ingestion - Kafka topic receives every new post. Decouples producers from moderation.
2. Classification pipeline - Text classifier (fine-tuned transformer) and image classifier (vision model) run in parallel.
3. Decision engine - Combines classifier outputs with user reputation score and platform policy rules.
4. Action layer - Executes the decision: allow, flag for human review, or block immediately.

Step 4: Deep Dives (12 min)

Text moderation model:

• Fine-tuned BERT or similar transformer on a labelled dataset of harmful content categories (hate speech, spam, self-harm, etc.).
• Served via a model server (Triton or TorchServe) with batching for throughput.
• Confidence threshold: block above 0.95, flag between 0.7 and 0.95, allow below 0.7.

Image moderation:

• Vision transformer classifying into categories: explicit, violent, safe.
• Pre-processing: resize, normalise. Inference on GPU instances behind an auto-scaling group.
• For edge cases, route to a human review queue.

Scaling considerations:

• Horizontal scaling of classifier pods behind a load balancer.
• Model versioning with canary deployments - roll out new models to 5 per cent of traffic first.
• Circuit breakers: if the classifier is down, default to "allow and queue for async review" rather than blocking all content.

Handling novel threats: In the short term, use keyword-based rules and human escalation. In the medium term, implement an active learning loop: human reviewers label new examples, which are fed back into the training pipeline for the next model version.

Phase 3: Behavioural (15 Minutes)

Question 1: "Tell me about a time you had to make a difficult technical decision with incomplete information."

Use the STAR framework:

• Situation: We needed to choose between building a custom ML pipeline and adopting a managed service, with a deadline in six weeks.
• Task: I was the tech lead responsible for the recommendation.
• Action: I ran a one-week spike comparing both approaches on our actual data. I documented trade-offs in a decision record and presented it to the team.
• Result: We chose the managed service, shipped two weeks early, and saved approximately £40,000 in engineering time over the first year.

Question 2: "Describe a time you disagreed with a teammate and how you resolved it."

• Situation: A colleague wanted to use a complex ensemble model; I advocated for a simpler baseline first.
• Task: Reach alignment without damaging the relationship.
• Action: I proposed we both implement our approaches in a time-boxed experiment and let the metrics decide.
• Result: The simpler model performed within 2 per cent of the ensemble at one-tenth the inference cost. We shipped the simple model and agreed to revisit if accuracy became a bottleneck.

Phase 4: Your Questions (10 Minutes)

Asking great questions shows judgement and genuine interest. Here are three strong examples:

1. "What does the on-call rotation look like for the ML platform team, and how do you handle model incidents?" - Shows you think about production realities.
2. "What is the biggest technical challenge the team expects to tackle in the next six months?" - Shows forward thinking.
3. "How does the team decide when to build in-house versus buy a managed solution?" - Shows pragmatism and architecture sense.

Avoid: Questions about perks, holidays, or anything easily found on the company website.

Phase 5: Wrap-Up (5 Minutes)

Thank the interviewer by name. Summarise your enthusiasm in one sentence: "I am genuinely excited about the content moderation challenges here - it is exactly the kind of high-scale ML systems work I want to be doing."

Send a brief thank-you email within 24 hours referencing one specific thing you discussed.

Self-Scoring Rubric

Rate yourself 1–5 on each dimension:

| Dimension | What "5" Looks Like | |-----------|-------------------| | Requirements gathering | Asked clarifying questions before jumping into design | | Architecture clarity | Drew a clean, logical system with clear data flow | | Depth of knowledge | Went deep on at least one component with real trade-offs | | Communication | Structured thoughts clearly; easy to follow | | Behavioural answers | Used STAR with specific, measurable results | | Questions asked | Showed genuine curiosity and good judgement |