AI Engineer Intern - Summer 2027
Posted Sep 9, 2026 · We last checked this listing on Oct 6, 2026
Likely interview questions for this role
Written from this job description, not a generic list. Each one notes what the interviewer is really checking.
About your past
Tell me about an AI or LLM project you built. What did not work the first time?
What they are checking: Hands-on experience and honesty about the trial and error that real AI work involves.
The internship focuses on internal tooling, not customer-facing models. Why does that interest you?
What they are checking: Whether they are motivated by practical engineering impact rather than only model training.
What would you do
One project is a generative assistant that proposes mitigation steps for alerts using approved observability sources. How would you start building it?
What they are checking: Whether they would learn the data and the alert workflow first instead of jumping straight to prompts.
You will report to the DevOps lead and partner with other teams. How would you get feedback from engineers who are busy?
What they are checking: Whether they seek out users early and make it easy for them to give input.
About the work
The assistant should trace its suggestions back to queries, dashboards or signals. Why does that matter, and how would you do it?
What they are checking: Understanding that engineers on call need to verify AI output, so grounding and citations matter.
What is retrieval-augmented generation, and when is it better than fine-tuning?
What they are checking: Basic grasp of how to give a model current, specific information without retraining it.
The observability stack includes Prometheus, Grafana, Loki, Tempo and Alertmanager. Which of these have you used, and what does each one hold?
What they are checking: Familiarity with the data sources the assistant would pull from.
How would you stop the assistant from suggesting an unsafe action on a production trading system?
What they are checking: Thinking about guardrails, read-only access and keeping a human in charge of real changes.
How would you measure whether the alert assistant is actually helping engineers?
What they are checking: Ability to define useful measures like time to resolve and how often suggestions were right.
The model gives a confident answer that is wrong. How would you design for that?
What they are checking: Awareness of hallucination and design choices like showing evidence and stating uncertainty.
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Practice this out loudThe full job description
As published by DV Trading.