AI Engineer Assessment Template
senior-level ai engineer assessment with hand-vetted prompts on llm integration (openai, anthropic, oss), retrieval-augmented generation, prompt engineering & evals, plus a reviewable session timeline. AI engineer hiring is the highest-velocity lane in tech right now; expectations and titles vary wildly between companies, which makes a calibrated rubric the only honest way to compare candidates.
What this template measures
Every skill needed for a ai engineer hire, covered across MCQ, coding, and essay questions.
LLM integration
LLM integration (OpenAI, Anthropic, OSS)
Retrieval-augmented generation
Retrieval-augmented generation
Prompt engineering
Prompt engineering & evals
Vector databases
Vector databases & embeddings
Agent frameworks
Agent frameworks (LangGraph, etc.)
Latency, cost, and safety tradeoffs
Latency, cost, and safety tradeoffs
Sample questions from this template
A preview of the questions you'll see when you use this template.
Which of these is the most idiomatic way to handle llm integration (openai, anthropic, oss) in production?
- A.Hand-rolled implementation with no library support
- B.Battle-tested library + thin abstraction
- C.Copy from the latest blog post
- D.Avoid the pattern entirely
A ai engineer reports a regression in retrieval-augmented generation. Which signal is MOST likely to identify the root cause?
- A.Application logs at INFO level only
- B.Recent deploy diff + relevant trace
- C.Number of open tickets
- D.Restarting the affected service
Implement a small module that demonstrates prompt engineering & evals. Include unit tests for happy path and one edge case.
Hint: Prefer clarity over cleverness; tests count.
Refactor the supplied snippet to fix a subtle bug in vector databases & embeddings without changing the public API. Explain the fix in 2–3 sentences.
Hint: Read the tests; they encode the contract.
In 200–300 words, describe how you'd evaluate a tradeoff between llm integration (openai, anthropic, oss) and agent frameworks (langgraph, etc.) on a real project.
Walk us through a recent ai project where retrieval-augmented generation was the deciding factor. (90 seconds)
Scoring rubric
How candidates are evaluated on this template.
Frequently asked questions
Who is this AI Engineer assessment template for?+
Hiring teams screening ai engineers at senior level. AI engineer hiring is the highest-velocity lane in tech right now; expectations and titles vary wildly between companies, which makes a calibrated rubric the only honest way to compare candidates. Use it for inbound applicants, sourced candidates, or as a take-home equivalent before live interviews.
Can I customize the AI Engineer template?+
Top to bottom. Add questions, remove ours, change weights, adjust difficulty mix, edit rubric language, and re-skin the candidate page with your brand. The AI Engineer template is software, not a fixed test.
Does this AI Engineer template include AI cheat detection?+
Built in. The AI Engineer template doesn't need extra setup for cheat detection — it's running silently from the candidate's first keystroke and surfacing flags only when something looks off.
Can ai engineers preview sample questions before the timer starts?+
Yes. The AI Engineer template supports unscored practice questions so candidates warm up before the timed section starts. You're measuring skill, not test anxiety.
How do I reuse this AI Engineer template across multiple jobs?+
Templates are first-class. You'll typically maintain one or two AI variants (e.g. mid vs senior) and clone the right one when a new req opens.
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