EmergingAI solution delivery, consulting and architecture

AI Evaluation Consultant

Also called AI Model Selection Consultant

An AI Evaluation Consultant helps customers choose the right model for their actual work by testing candidates against representative tasks. It matters because a well-evidenced choice avoids costly rework and failed deployments.

What they do

They start by working out what the customer actually needs the model to do. Then they build a set of representative tasks and run several candidate models against them. They compare quality, cost, speed, safety behaviour and fit with existing systems. They build small proof-of-value prototypes where a paper comparison is not enough. They write up the tradeoffs and recommend a choice, with the reasoning set out so another person could rerun the work. They present findings to technical and non-technical audiences, and they flag risks the customer has not yet considered. Much of this work sits today inside solution architect and consulting jobs.

What a strong hire delivers

In the first months, a strong hire produces a repeatable evaluation method the team can reuse across customers. They deliver at least one comparative evaluation that ends in a clear, documented recommendation. They tighten how the company talks about model choice, so sales and delivery give consistent answers. They also leave behind task sets, scoring approaches and templates that others can pick up without them.

How you can tell they are good

Ask them to walk through a past model selection from the first conversation to the final recommendation. Listen for how they built the task set, how they handled disagreement and what they would change. A strong candidate shows their evidence and admits where it was thin. Be wary of anyone who talks about leaderboards and general benchmarks but cannot explain why those results suit the customer's work.

Why it is hard to fill

Almost nobody carries this title yet. The work is usually done by solution architects, consultants or delivery engineers who picked it up because customers kept asking. That makes candidates hard to find by title and hard to compare. The mix is unusual: technical depth, experimental discipline and client-facing credibility. Many people have two of the three. Expect to search by what someone has done, not by what their last job was called.

Where to find them

Look in solution architecture, technical consulting, systems integration and pre-sales engineering. Applied AI and delivery teams at consultancies, integrators and model providers are good places to start. Some come from data science or machine learning engineering with a strong client-facing streak. Product-minded engineers who have run model bake-offs inside a company are also worth speaking to, even if their title says something else.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for an AI Evaluation Consultant

  1. Tell us about a time you helped a customer choose between models. How did you decide what to test?

    Strong answers start with the customer's real tasks and constraints, then describe how the test set was built and checked. Worry if they lead with public benchmarks, or cannot say how the tasks reflected actual use.

  2. How do you make an evaluation reproducible, so someone else could rerun it and get the same picture?

    Look for versioned task sets, fixed settings, recorded prompts and scoring rules, and notes on variance. Worry about answers that rely on judgement alone or on results that live in one person's notebook.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We help customers choose and deploy AI models that fit their real work. We are hiring an AI Evaluation Consultant to test candidate models against representative tasks and turn the results into clear, documented recommendations. You will work with customers, architects and engineers on decisions that shape what gets built.

What you will do

  • Work with customers to define the tasks and success criteria a model must meet
  • Design representative task sets and run comparative evaluations across models
  • Build small proof-of-value prototypes to test fit with customer systems
  • Document the selection rationale and tradeoffs so results can be reproduced
  • Present findings and recommendations to technical and business audiences
  • Identify and explain deployment risks, including safety and integration issues
  • Create reusable evaluation methods and templates for the wider team

What you will bring

  • Evidence of leading model or technology selection work for a customer or internal team
  • Experience designing evaluations with clear, repeatable scoring
  • Background in solution architecture, consulting, systems integration or technical delivery
  • Experience integrating tools and protocols into working solutions
  • Clear written and spoken communication with non-technical stakeholders
  • Practical judgement about AI risk and where evidence is weak

Nice to have

  • Experience in pre-sales or solution design for customers
  • Familiarity with agent frameworks and orchestration approaches
  • Experience building proof-of-value prototypes quickly

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Sales and solution architectureAdvancedMatch technical solutions to validated customer needs
  • Tool and protocol integrationAdvancedConnect models to external data and capabilities
  • AI risk managementWorkingIdentify, assess, treat and monitor AI risks
  • Technical communicationWorkingExplain evidence and limitations to relevant readers

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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