AI Solutions Architect

Also known as

The market uses several titles for the same role:

How it changes by seniority

• Mid — Designs a solution’s architecture (models, data, integrations) with guidance.
• Senior — Decides build vs buy and the stack; ensures it scales, is secure and maintainable.
• Lead — Sets architecture standards and the platform across the whole portfolio.

Interview questions

1. 1. A customer wants "AI in their support service". How do you get from that sentence to an architecture?

   What to look for: Grounds the problem in concrete processes, data constraints and build-vs-buy before drawing anything.

2. 2. When do you recommend building custom and when buying a tool?

   What to look for: Decides by whether the process is a competitive edge and by control of cost/data/reliability.

3. 3. How do you design so the solution holds when the provider changes or retires a model?

   What to look for: Thinks model abstraction, regression evals and a maintenance plan from the design.

4. 4. Which architecture decisions do you often see turn expensive at six months?

   What to look for: Anticipates debt: provider coupling, missing observability, broad permissions.

Scorecard

• Problem to design — Translates a fuzzy need into processes, data and concrete architecture.
• Build-vs-buy — Decides build or buy by competitive edge and control of cost/data.
• Design for change — Abstracts the model and plans maintenance; the solution survives provider changes.
• Total-cost view — Counts integration, supervision and maintenance, not just the model.

Red flags

• — Jumps to technology before understanding the process and the data.
• — Couples everything to one provider with no plan for when it changes.
• — Designs with no observability or evals: no way to operate what they propose.