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AI Solutions Engineer
Also called AI Sales Engineer
An AI Solutions Engineer shows customers that an AI product fits their technical needs, then turns what they learn into clear requirements. They help the company win the right deals and see them reach useful deployment.
What they do
They run discovery with technical and business stakeholders to understand the systems, data and constraints involved. They build and deliver demonstrations tied to the customer's real use case, not a generic tour. They answer hard technical questions on security, reliability, integration and limits, and say so plainly when the product is not a fit. They write down implementation requirements, test assumptions with usage data, and build simple value models. They pass clear feedback to product and engineering, and stay close to the deal until deployment is under way.
What a strong hire delivers
In the first months, a strong hire gives the sales team demos built around customer problems rather than features. Discovery notes become clear, agreed requirements. Technical objections get consistent, honest answers. Weak-fit deals are spotted early. Product gets organised customer feedback, and early customers move from a successful demonstration to a deployment they actually use.
How you can tell they are good
Ask them to walk through a recent demo from discovery to outcome. Listen for how discovery shaped what they showed, and what they chose to leave out. Ask about a deal they advised against. A strong candidate explains technical trade-offs simply, admits product limits without defensiveness, and can describe the requirements they handed over and what happened next.
Why it is hard to fill
The title is used loosely. Some companies mean a pre-sales demonstrator, others a hands-on engineer who builds prototypes. Candidates rarely have all of it: technical depth, commercial judgement and comfort with AI limits. Strong people from traditional software pre-sales often lack AI experience, while technical people can struggle in a customer room. Good ones are usually settled and not looking.
Where to find them
Most come from solutions engineering or sales engineering at enterprise software, data or cloud companies. Others move across from technical customer success, partnerships, implementation consulting or enterprise sales with a technical bent. Look at companies selling developer platforms, analytics, automation or security products. Teams at model providers and agent framework businesses are a natural source, though competition for them is high.
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 Solutions Engineer
- Describe a discovery process you ran for a technical buyer. What did you learn that changed what you demonstrated?
Strong answers show specific questions about data, systems, risk and success criteria, and a demo that changed as a result. Worry if they describe a standard script, or talk only about features shown.
- Tell me about a deal where you told the customer the product was not a fit. How did you handle it?
Look for honesty, tact and commercial awareness. They should explain the reasoning and the outcome. Be wary of anyone who has never done this, or who treats every objection as something to overcome.
4 more questions, with what a strong answer covers, are in the Role Kit.
Job description you can post
We are hiring an AI Solutions Engineer to help customers see how our AI product fits their technical needs. They will work alongside sales, product and engineering, running discovery, delivering demonstrations and setting clear requirements for deployment.
What you will do
- Run technical discovery with customer stakeholders to understand systems, data and goals
- Build and deliver demonstrations tailored to each customer's use case
- Answer technical questions on security, integration, reliability and limits
- Write clear implementation requirements and hand them to delivery teams
- Build simple value models and use usage data to track progress
- Share structured customer feedback with product and engineering
- Advise the sales team on technical fit and deal risk
What you will bring
- Experience delivering technical demonstrations that led to real customer decisions
- Examples of discovery work that shaped a solution
- Ability to explain technical trade-offs clearly to mixed audiences
- Working knowledge of APIs, data integration and solution architecture
- Comfort with commercial conversations and basic financial analysis
- Willingness to say when a product is not the right fit
Nice to have
- Hands-on experience with agent frameworks or model-based applications
- Background in enterprise sales, partnerships or customer success
- Experience supporting a customer from pilot to production
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
- Commercial and financial analysisAdvancedAssess unit economics and value realisation
- Technical communicationWorkingExplain evidence and limitations to relevant readers
- Product managementWorkingChoose problems and manage product outcomes
Other roles in aI commercial sales and customer success
Related roles in other families
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.
- Careers: jobs, Anthropic
- What is the Model Context Protocol?, Model Context Protocol contributors
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Role Kit
AI Solutions Engineer: the Role Kit
- What the role is and what a strong hire delivers
- Every interview question, with what a strong answer covers
- A ready-to-post job description
- Where to find the people, and the other titles they use
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