Hiring nowAI solution delivery, consulting and architecture

AI Solutions Architect

Also called GenAI Solutions Architect · Applied AI Architect

An AI Solutions Architect designs AI systems that fit a customer's real environment, risk controls and goals. They turn loose requirements into something that can be built, supported and shown to work.

What they do

Starts with the business problem and the constraints around it: data access, security, audit needs and existing systems. Then sketches the architecture, picks model patterns and integration routes, and writes down the tradeoffs. Builds small proof-of-value prototypes to test the risky parts early. Runs security and risk reviews with the customer's teams. Explains the design to engineers, buyers and compliance staff in language each can act on. Hands over a validation plan so everyone agrees how success will be measured before build begins.

What a strong hire delivers

Within the first months, a strong hire gives the team a clear reference architecture for the most common customer needs. They close at least one stalled deal or project by resolving a security or integration concern. They leave behind written tradeoff analyses, a repeatable validation plan, and prototypes that show what works. Delivery teams spend less time on rework because requirements were covered up front.

How you can tell they are good

Ask them to walk through a past design and explain what they rejected and why. Good candidates name the tradeoffs, the risks they found and how they tested them. Ask how they would prove an AI system met a control requirement. Be wary of people who talk only about models, skip integration and security, or cannot say what was measured after deployment.

Why it is hard to fill

The title varies between employers, so searches on it miss good people. The work sits between engineering, consulting and sales, and few people have done all three. Many architects know cloud but not LLM patterns. Many AI engineers have never faced a customer security review. Employers compete for the same small group, so strong candidates are often already in demand and choose carefully.

Where to find them

Look at solution architects and pre-sales engineers from cloud providers, systems integrators and enterprise software firms. Consultants in technical delivery and integration are another source. Some come from AI labs in customer-facing technical roles. Others are senior engineers who moved into architecture after building production LLM applications. Security-minded architects who have added AI work are especially worth a conversation.

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 Architect

  1. Describe an AI or complex integration solution you designed. What were the main requirements and what did you build?

    Strong answers state the business need, the constraints and their own part clearly. They show how requirements shaped the design. Worry if the answer is a list of technologies with no link to the problem or no stated outcome.

  2. Tell me about a design option you rejected. What tradeoffs drove that choice?

    Look for honest comparison of cost, latency, risk, supportability and effort. Good candidates admit what they gave up. Worry about candidates who claim their design had no downsides or cannot recall alternatives.

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

Job description you can post

We help customers turn AI ideas into solutions that work in their real environment. We are looking for an AI Solutions Architect to shape designs, test them early and guide delivery teams. The work mixes technical depth with clear communication and sound judgement about risk.

What you will do

  • Turn customer requirements into integrated AI solution designs
  • Write architecture documents and tradeoff analyses
  • Build proof-of-value prototypes to test key risks
  • Lead security and risk reviews with customer teams
  • Define validation plans and measures of success
  • Support delivery teams through build and deployment
  • Explain designs to technical and non-technical audiences

What you will bring

  • Designed and delivered integrated solutions in a customer or enterprise setting
  • Hands-on experience with LLM application patterns
  • Working knowledge of cloud architecture and system integration
  • Experience handling security or compliance reviews
  • Clear written and spoken technical communication
  • Evidence of measuring outcomes after deployment

Nice to have

  • Experience with agent frameworks and tool integration protocols
  • Background in pre-sales or technical consulting
  • Familiarity with AI risk management practices

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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