How to Hire an AI Engineer for Production

Start with the work your team needs done

I delivered a multi-agent platform for a private-equity client through Ilayer. It connected to existing business systems and used FastAPI, Postgres/pgvector and Amazon Bedrock. The client remains confidential.

That experience shapes how I would hire for the next project: start with the workflow the engineer must own. A polished chat demo tells you very little about permissions, stale source data or what happens when an integration goes down.

To hire an AI engineer for production, define the task, the allowed actions and the evidence that will make you accept the delivery. Then look for someone who can build and operate the surrounding software.

Anthropic distinguishes workflows with predefined paths from agents that choose their next steps. Its agent engineering guide recommends starting simple. I would ask a candidate which parts of my workflow need a model at all.

The production AI hiring scorecard

I would assess four areas in the interview. For each one, ask for a concrete artifact or a walkthrough of a sanitized example. An engineer can respect an NDA and still explain their decisions.

Bedrock exposes retrieval filters for metadata conditions. That is a useful mechanism; deciding which conditions an authenticated user is allowed to request remains application work. I would test that distinction during the interview.

For the evaluation discussion, use my release-gating evaluation walkthrough. Ask the candidate which checks belong in deterministic code and which require judgment. An aggregate accuracy percentage should never hide a permission failure.

Run a small, paid working session

I prefer a bounded exercise to an unpaid production build. Give the candidate a sanitized example and agree the timebox and compensation first. The objective is to observe decisions, including what they decline to automate.

Here is an illustrative exercise for a document assistant. It is a hiring scenario, not a report of a client system or a complete security assessment.

1. 01Supply a current document, an obsolete revision and a question whose answer is absent. Mark which user can access each file.
2. 02Ask for a minimal read-only answer path. Require the candidate to state what counts as a supported answer before building it.
3. 03Change the requesting user and remove a source. Observe whether the system refuses appropriately and explains the missing evidence.
4. 04Ask for the next delivery step, the largest unresolved risk and an estimate with explicit assumptions.

The strongest signal is often the explanation after a failed case. Can the engineer locate the failure in retrieval, extraction, authorization or generation? Can they propose a test that will catch its return?

Write a brief someone can estimate

A freelancer, consultant and permanent hire all need the same initial context. I would send the brief below with a role description. Leave unknowns visible so discovery can resolve them.

1. 01Outcome: name one user, one recurring task and the current process. State how you will compare the new workflow with that baseline.
2. 02Inputs: list systems, document types, source owners, access restrictions and whether representative test data is available.
3. 03Authority: separate read-only answers, suggested actions and actions the software may execute. Name the human approver for consequential writes.
4. 04Acceptance: agree evaluation cases, failure handling, cost measurement and the person who signs off. Derive thresholds from your own risk.
5. 05Delivery: state your environment, target date, review availability, expected documentation and support after handover.

If those inputs are uncertain, commission a short discovery phase with tangible outputs: a data-access map, a tested integration assumption, an initial evaluation set and a scoped proposal. Its duration depends on your access constraints. A fixed delivery promise before examining them is not a useful estimate.

Decide who owns the system after launch

I would choose the engagement format around ownership. A bounded mission works when a team can receive and maintain the result. A permanent role makes more sense when AI engineering is an ongoing product responsibility.

My work combines production AI engineering and integration with experience in systems where actions need clear permissions. If that matches your project or role, send me the workflow and the brief. You can begin with the business problem; you do not need to have selected an AI stack.

AI engineer hiring questions