Interview scorecard template

AI Knowledge Engineer interview scorecard

Evaluate AI Knowledge Engineer candidates across 4 weighted areas: technical proficiency, systems and trade-offs, evidence and rigour, and collaboration and communication. Technical proficiency leads at 35%, so check hands-on command of RDF/OWL, SHACL shapes, SPARQL or Cypher, and triple stores like GraphDB or Neo4j, plus embedding models and chunking strategies used. Use the rubric to compare role-specific evidence consistently.

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software dataknowledge graphsontology engineeringrag pipelinessparql
TL;DR
For technical proficiency, look for evidence the candidate names specific ontologies authored or extended, writes non-trivial SPARQL from memory, and explains SHACL validation versus OWL inference precisely. For systems and trade-offs, look for evidence the candidate justifies modelling decisions against query patterns and ingestion cost, and admits where a simpler flat schema would have served better. Apply the written 1–5 anchors to every answer, record the evidence behind each rating, and use the factor weights to reach a consistent overall assessment.
Complete evaluation framework

What to assess and how to score it

Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.

01
Evaluation factor

Technical proficiency

35% weight

Check hands-on command of RDF/OWL, SHACL shapes, SPARQL or Cypher, and triple stores like GraphDB or Neo4j, plus embedding models and chunking strategies used in retrieval pipelines.

Evidence to listen for

  • Command of the languages, frameworks, and data tools the role actually uses
  • Understands correctness, performance, and failure modes, not just syntax
  • Has opinions on testing and can justify them
  • Reads and reasons about code they did not write

Five-point scoring guide

1
Poor

Cannot work independently; fundamentals are missing.

2
Needs Improvement

Weak fundamentals; output needs heavy review.

3
Satisfactory

Competent for the role; needs guidance on complex or unfamiliar work.

4
Very Good

Strong practitioner; handles hard problems with little guidance.

5
Excellent

Names specific ontologies authored or extended, writes non-trivial SPARQL from memory, and explains SHACL validation versus OWL inference precisely.

02
Evaluation factor

Systems and trade-offs

25% weight

Probe design choices: property graph versus RDF, taxonomy depth, entity resolution thresholds, when to use graph retrieval over pure vector search, and index refresh cadence.

Evidence to listen for

  • Reasons about scale, latency, cost, and failure before writing code
  • Names the trade-off they chose and what they gave up
  • Understands the data lifecycle end to end
  • Anticipates what breaks at ten times the volume

Five-point scoring guide

1
Poor

No thinking beyond the immediate task; no awareness of scale or failure.

2
Needs Improvement

Limited architectural awareness; struggles with design decisions.

3
Satisfactory

Works within an existing design; makes sound local decisions.

4
Very Good

Designs for scale and maintainability; articulates trade-offs clearly.

5
Excellent

Justifies modelling decisions against query patterns and ingestion cost, and admits where a simpler flat schema would have served better.

03
Evaluation factor

Evidence and rigour

25% weight

Test how they measured retrieval quality: gold question sets, recall@k, groundedness or hallucination rates, SME review loops, and regression checks after ontology changes.

Evidence to listen for

  • Validates results rather than trusting output
  • Knows how their work is measured and what a bad result looks like
  • Can describe a time their own analysis or model was wrong and how they caught it
  • Careful about data quality, leakage, and silent failure

Five-point scoring guide

1
Poor

Ships unvalidated work; no notion of how correctness is checked.

2
Needs Improvement

Validates superficially; misses obvious quality or leakage issues.

3
Satisfactory

Reasonable checks in place; rigour drops under time pressure.

4
Very Good

Validates thoroughly; can name a real error they caught in their own work.

5
Excellent

Cites baseline and post-change numbers on a named evaluation set, and describes catching a regression before it reached users.

04
Evaluation factor

Collaboration and communication

15% weight

Assess elicitation work with subject matter experts: competency questions, terminology disputes, curation workflows, and handing schemas to application engineers or data stewards.

Evidence to listen for

  • Explains technical work to non-technical stakeholders
  • Gives and takes code or peer review constructively
  • Documents enough that the work survives their absence
  • Aligns with team process rather than working around it

Five-point scoring guide

1
Poor

Cannot work in a team; resistant to feedback.

2
Needs Improvement

Communication issues create rework; lone-wolf tendencies.

3
Satisfactory

Adequate team member; documentation and review participation are light.

4
Very Good

Communicates well; reliable reviewer and collaborator.

5
Excellent

Describes running competency question workshops, resolving conflicting SME definitions, and documenting the model so downstream teams queried it unaided.

Evidence-led prompts

Interview questions for a AI Knowledge Engineer

Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.

  1. 01

    Give an example of an AI project you have worked on successfully.

  2. 02

    What experience do you have with natural language processing?

  3. 03

    Can you explain your experience with machine learning methods?

  4. 04

    How do you handle data preparation for training?

  5. 05

    Can you describe your approach to feature engineering?

See the complete AI Knowledge Engineer question set
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