Hiring Forward-Deployed AI Engineers: Look for Delivery Evidence

By Sam M. Sweilem · LockedIn Labs · September 8, 2026

A forward-deployed engineer works at the boundary between a customer's operating problem and a production system. Hiring for that role requires more than a list of models, frameworks, or coding assistants. The useful question is whether the candidate can turn an ambiguous problem into accepted, maintainable work.

For an AI implementation team, recruiting is an engineering decision. The skills selected during hiring shape the architecture, the review burden, and the quality of the eventual handover. Here is the evidence I would ask a hiring team to examine.

Begin with the work the person will own

Describe the actual assignment before writing the title. A platform engineer improving an internal developer workflow, an applied AI engineer integrating retrieval, and an FDE embedded with a business owner may use overlapping tools while carrying different responsibilities.

State the expected decisions, customer interaction, data boundaries, deployment duties, and support ownership. Include the systems the engineer must learn. A clear role brief makes both recruiting and candidate self-selection more accurate.

Use a bounded, representative working session

Give the candidate a small scenario with synthetic data, a documented interface, and a visible acceptance goal. For example, ask them to design a service-case assistant that retrieves approved reference material, proposes a response, and routes uncertain cases to a reviewer.

Explain the evaluation criteria in advance. Allow appropriate AI tools when they reflect the job. Assess the candidate's decisions and verification, rather than treating access to a coding assistant as either proof of ability or a disqualification.

Keep the exercise proportionate. It should be a discussion and work sample, not unpaid production work. Give candidates a fair opportunity to explain tradeoffs and use an equivalent alternative when the format creates an accessibility barrier.

Look at the questions before the code

A strong candidate asks which sources are authoritative, who accepts the result, what happens when a tool fails, and which actions require approval. They identify assumptions before embedding them in software.

Ask what they would refuse to automate initially. The answer should connect to a specific consequence or missing control. Then ask what evidence would let them expand the scope. That second question separates a thoughtful boundary from an indefinite objection.

Inspect the artifacts that make delivery repeatable

• Problem brief: the user's task, constraints, baseline, and acceptance criteria.
• Integration: a working path through the required systems with explicit permissions and failure handling.
• Evaluation: representative examples, negative cases, and a clear account of what has and has not been tested.
• Release record: the candidate version, checks, unresolved issues, and approval decision.
• Handover: setup instructions, operating ownership, and a recovery path another engineer can use.

The LockedIn Labs AISDLC-CQ specification is a reference for code quality and verification discipline. Use the requirements relevant to the assignment; do not turn an entire framework into an indiscriminate interview checklist.

Separate learning activity from demonstrated capability

Course completion, portfolio claims, and observed work are different evidence. Record what the hiring team actually saw and avoid treating a training credential as a substitute for a work sample.

The LockedIn FDE Benchmark publishes a versioned framework for discussing role evidence. Read its current methodology and status when using it. It is a LockedIn Labs initiative with a published separation between training and benchmark credit; an owned framework is not an independent endorsement of a candidate or the firm.

For development after hiring, LockedIn Labs training offers practical learning pathways. Connect the pathway to observed gaps and supervised work, then reassess the artifacts produced.

Choose the engagement model deliberately

A permanent hire builds internal capability. Staff augmentation adds capacity under the client's delivery leadership. An embedded engineering engagement can take responsibility for a bounded implementation and its transfer. Decide which ownership model the business needs before comparing resumes or rates.

LockedIn Labs' forward-deployed engineering model explains how the firm approaches embedded delivery. The selection question remains the same across engagement types: who owns the accepted outcome, and what evidence will show that the team can deliver it?

A worksheet for the next review

Use the public FDE hiring evidence worksheet from LockedIn Labs AI Engineering Notes to capture the decision, evidence, and remaining questions. The companion repository contains four original notes by Sam M. Sweilem.

About the author. Sam M. Sweilem is an enterprise AI systems leader and the founder of LockedIn Labs. His writing covers AI implementation, transformation, agentic workflows, and engineering leadership.