Hiring a generative AI engineer requires looking beyond a successful demonstration. You need to understand how a candidate builds, evaluates, and operates a feature when inputs vary and outputs are not always predictable.
A demo may show a chatbot answering a prepared question. A product must also handle missing information, unexpected requests, slow responses, service failures, and users with different permissions.
The right hiring process explores those situations before you choose an engineer.
Define the generative AI application
Start by describing the workflow you want to support.
Generative AI projects can include:
- Assistants that answer questions using approved documents.
- Tools that create or revise content.
- Systems that extract structured information.
- Features that summarize conversations or reports.
- Applications that propose or carry out actions through other software.
Each application introduces different requirements.
A writing assistant may need careful control over tone and formatting. An internal knowledge assistant needs to retrieve relevant information and respect access restrictions. A system that takes actions needs clear boundaries, validation, and a plan for handling mistakes.
Your role description should make those requirements visible.
Look for evidence of delivery beyond the prototype
Ask candidates to explain a project from initial implementation through testing and use.
Focus on their personal contribution. What did they build? Which parts were experimental? How did they decide whether the application worked well enough? What problems appeared outside the demonstration?
Candidates with relevant experience should be able to discuss specific failures and the changes made in response.
For example, an assistant may have retrieved an outdated document, produced an answer without enough supporting information, or returned an output that another service could not process.
Understanding how the engineer investigated the problem is more useful than a list of tools on a résumé.
Assess retrieval when your product depends on documents
Retrieval-augmented generation, often called RAG, gives a model retrieved information to use when producing a response.
If your application depends on this approach, the engineer should understand the work around retrieval as well as the model call.
Discuss document processing, finding relevant material, handling updates, applying permissions, and showing supporting sources where the product requires them.
Use a practical interview scenario:
“A user asks a question that combines information from two documents, but only has permission to access one. How would you design and test the response?”
This creates a useful conversation about retrieval, access control, and what the system should do when it cannot provide a complete answer.
Ask how quality will be measured
Generative AI hiring should include a discussion of evaluation.
Ask the candidate how they would create test cases and compare changes. What would they measure? Which failures would block release? How would they investigate a regression?
A useful evaluation set should reflect the intended workflow. Include ordinary requests, incomplete inputs, ambiguous questions, and cases in which the application should decline to answer or ask for clarification.
For an extraction tool, you might inspect field-level accuracy and handling of missing values. For a document assistant, you might inspect whether the retrieved material supports the answer.
The engineer should connect the evaluation method to the feature’s purpose.
Explore integrations and action boundaries
Some generative AI applications suggest text. Others interact with external systems.
When an application can create records, send information, or trigger another workflow, ask how those actions are controlled.
Relevant topics include:
- Validating inputs and generated outputs.
- Restricting available actions.
- Requesting human approval where appropriate.
- Preventing duplicate actions during retries.
- Recording enough information to investigate failures.
An engineer should be able to explain how an application moves from a proposed action to an authorized, validated operation.
Set a focused first milestone
A useful first engagement has a clear scope and a decision at the end.
For example, ask the engineer to build and evaluate a limited document assistant using a representative sample of approved material. Define the expected behavior, test cases, and known constraints.
Review the results before expanding the project. This helps the team identify what works, what needs improvement, and which assumptions require further testing.
Frequently asked questions
Is prompt engineering enough for this role?
For a complete product feature, responsibilities may extend to data processing, backend integration, testing, deployment, and monitoring. Match the role to those responsibilities rather than assuming prompting covers the whole system.
Should I require experience with a specific framework?
Require it when it is essential to your existing environment. Otherwise, explore the candidate’s understanding of the underlying problems and ability to explain implementation decisions.
How can I compare candidates fairly?
Give candidates the same core scenario and assess them against consistent criteria: relevant experience, technical reasoning, evaluation, failure handling, and communication.
Find generative AI talent through MENA Engineers
MENA Engineers connects businesses with engineering talent from the Middle East and North Africa.
Tell us what your generative AI feature should do, what you have built so far, and which responsibilities the engineer will own. Start with a defined use case and build your talent search around it.