Find applied AI engineers by looking for ownership of a working system: who used it, what could go wrong, how its quality was measured and what the engineer changed after release. A model name or a polished demonstration is a starting point for that conversation, not its conclusion.
This guide is about reading profiles and deciding whom to approach. It complements a practical assessment by giving your sourcing team a clearer definition of relevant experience. You should be able to explain why someone belongs on the shortlist before asking them to complete an exercise.
Start with the distinction between AI engineers, research engineers and research scientists. An engineer integrating existing models into a product can be exactly the right hire. Training a new model is a different requirement, not a higher score on the same scale.
Write the production problem before the search query
A useful search brief describes the system the hire will own and the constraints that make it difficult. That changes which backgrounds deserve attention.
For a customer-facing assistant, the important experience may be application engineering, retrieval permissions, evaluation and graceful failure. For a document-processing product, it may be extraction accuracy, exception handling and integration into an existing workflow. For a serving team, the central questions may be capacity, response time and resource cost.
Write a short paragraph covering the user, input, decision or action, and consequences of failure. Then list what already exists and what the person must build next. This is the AI-specific version of defining a founding engineering brief.
Avoid turning that paragraph into a catalogue of fashionable frameworks. Someone who has solved a closely related operational problem with different tools may be more useful than someone who has used every tool in your stack on demonstrations.
Read for decisions, not only outputs
A profile becomes informative when it connects a technical change to the reason for making it. Look for evidence that the candidate understood the problem, selected an approach and took responsibility for the result.
| Profile signal | Follow-up worth asking |
|---|---|
| Shipped a model-backed feature | Who used it, and what happened after launch? |
| Built an evaluation suite | Which failures did it catch, and what changed because of them? |
| Reduced model cost | What did they measure, and what quality trade-off did they accept? |
| Improved retrieval | How did they separate retrieval failures from answer-generation failures? |
| Operated an agent | What could it change, and how were permissions and recovery handled? |
| Fine-tuned a model | What baseline was insufficient, and how was improvement checked? |
These are prompts for clarification, not mandatory CV phrases. Confidential work may leave little public evidence. Give the candidate a way to explain a design, incident or trade-off without sharing a former employer’s code or data.
Look for evaluation that changed a release decision
Evaluation experience matters when it influenced what shipped. A candidate should be able to describe the cases they tested, the behaviour they cared about and how results affected a decision.
For an agent, a convincing final response is different from successfully completing the underlying task. Evaluation guidance distinguishes what an agent says from what happens in its environment. That gives you a useful sourcing question: has this person checked outcomes, tool use and side effects, or only reviewed generated text? [Source 1]
A portfolio can show this through a small test set, a failure log and a description of a change made after testing. A large benchmark score with no explanation of the data or evaluation conditions is less informative about your product.
During a first conversation, ask for one example where the evaluation blocked a release or reversed a decision. The answer helps distinguish ownership from participation in a project that someone else designed.
Find the operational work around the model
Production experience includes the surrounding software and the decisions needed to keep it useful. Search beyond the model integration itself.
The ML Test Score research treats testing and monitoring as substantial parts of production readiness, covering data, models, infrastructure and ongoing behaviour. Use those areas to broaden your search beyond a single title. [Source 2]
Relevant experience may sit under backend engineering, search, recommendation systems, developer tools, machine learning infrastructure or data platforms. Ask whether candidates have handled changing inputs, dependency failures, access boundaries, version changes and incidents.
For example, a useful discussion might follow a request when its upstream model is unavailable. Does the system retry, fall back, queue work, ask for human review or stop? Who can see the failure, and what does the user experience? There is no universal answer, but there should be a deliberate one.
Do not confuse complexity with depth
An experienced engineer should be able to explain why a simpler approach was sufficient. Official agent-building guidance recommends starting with the simplest solution that works and adding complexity when the task warrants it. [Source 3]
That means a well-operated workflow can be stronger evidence for your role than a complicated autonomous agent. A candidate who chose a deterministic rule for a narrow task may have exercised better judgement than one who used a model everywhere.
Look for a clear account of alternatives. What was considered? What was measured? Which part needed a model, and which part benefited from ordinary software? A useful profile shows restraint as well as capability.
Google’s Rules of Machine Learning similarly emphasises simple baselines, dependable pipelines and attention to the surrounding engineering. It provides a practical counterweight to sourcing entirely through model novelty. [Source 4]
Make a shortlist with different routes into the work
Build the shortlist around relevant evidence while allowing more than one career path. This is Corellia Talent’s recommended sourcing framework.
One route is an application engineer who has taken an AI feature from prototype to regular use. Another is an ML engineer who has operated models and now works with language-model applications. A third is a search or data engineer with strong retrieval and evaluation experience. A fourth may be a researcher who has repeatedly built usable systems with product colleagues.
For each person, record the strongest evidence, the missing information and the first question you would ask. Keep the evidence separate from the confidence of the writing. A quiet profile with one well-explained system can deserve more attention than an impressive list of unconnected projects.
Do not require public GitHub activity as a proxy for professional quality. A candidate can discuss private work through architecture, decisions and lessons. Public repositories are useful when available, but they are one evidence channel.
Make the first message specific to their work
A good approach connects a candidate’s experience to an actual problem. State the system you are building, the responsibility on offer and the reason their background appears relevant.
Instead of a generic request for LLM experience, explain that the role owns a customer workflow where reliability, permissions or evaluation is the unresolved challenge. Mention the relevant part of their work without pretending to know more than their public profile shows.
Be clear about location, working arrangement and the scope of ownership. The salary guide can help structure the package conversation, while the AI and machine learning recruitment page sets out the disciplines Corellia covers.
Keep a record of what attracts or discourages relevant candidates. Repeated questions about product ownership, evaluation resources or access to users can reveal a weakness in the brief before it becomes a late-stage problem.
Turn sourcing evidence into a focused interview
The first technical conversation should resolve the largest uncertainty in the profile. If the candidate clearly built the application but their evaluation ownership is unclear, explore that. If they operated a model service but product judgement is untested, ask about users and trade-offs.
Use a small project discussion rather than an encyclopaedic quiz. Give the same core questions to candidates for the same role, and record evidence against the agreed responsibilities. That makes the shortlist easier to compare without forcing every person into an identical career history.
The aim is a hiring decision you can explain: this engineer has dealt with the problems your product is about to encounter, and the remaining gaps are specific enough to assess.
If you are hiring for applied AI, Start a conversation.
Sources
- Anthropic: Demystifying evals for AI agents. Outcome-based evaluation and evaluation layers. Checked 31 October 2026.
- Google Research: The ML Test Score. Original production-readiness rubric. Checked 31 October 2026.
- Anthropic: Building effective agents. Starting with simple systems and selecting appropriate complexity. Checked 31 October 2026.
- Google: Rules of Machine Learning. Engineering practices, baselines and pipelines. Checked 31 October 2026.