Don't screen for buzzwords. Screen for ownership.
AI engineering interviews should test how someone makes decisions when systems fail, data is imperfect, access is constrained, and outcomes must be measured.
Discuss role requirementsWhat strong engineering signals look like.
We recommend matching practical assessments to the responsibilities of each role rather than using the same generic coding test for everyone.
Forward Deployed
Enterprise system design, integration debugging, customer discovery, and deployment trade-offs.
Data Engineering
SQL, pipeline failures, schema evolution, orchestration, quality, and data access.
Applied AI
Retrieval design, evaluation sets, inference reliability, product APIs, and data privacy.
Agentic AI
State, tools, approvals, idempotency, observability, and bounded autonomous behavior.
Ask for engineering trade-offs.
A meaningful evaluation asks candidates to explain how they would validate quality, operate the system, and recover when reality differs from the happy path.
Sample assessment areas
- Architecture walkthrough: design for realistic constraints
- Debugging scenario: locate and contain production failures
- Quality strategy: define measurable evaluation signals
- Security review: identity, secrets, least privilege, and auditability
- Communication: explain trade-offs to technical and business stakeholders
Assessment format and ownership are agreed with each client during role scoping.
Build a scorecard before a shortlist.
Use a rubric that reflects the work, not just frameworks on a résumé.
How to vet AI engineers without confusing a prototype with production experience
Practical questions for evaluating ownership, architecture, safety, and delivery judgment.