A solutions architect in a go to market team usually pairs deep technical skill with customer facing work: scoping integrations, writing sample code, and helping customers get an API into production. A strong candidate brings hands on software experience, comfort explaining technical tradeoffs to both engineers and business buyers, and the ability to turn vague customer needs into working designs. For a search API built for AI models and agents, that means understanding how retrieval fits into AI applications and being able to show it working.
How it fits the AI era
AI is changing this work because customers now arrive with AI features already in mind and expect help wiring retrieval and search into models and agents. The architect spends less time on basic setup and more on design questions like relevance, evaluation and reliability. What stays human is judgment about the customer's real problem, trust building, and clear explanation of tradeoffs.
How it builds AI experience
The role offers direct exposure to how AI models and agents use search, including retrieval quality, evaluation and API integration patterns. That experience is built by working on real customer integrations and studying how retrieval affects model output. Candidates can strengthen it with personal projects that connect a model to a search API and measure the results.
Worth showing
- Customer facing technical work
- Retrieval and search basics
- Sample code and demos
- Clear technical explanation
This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.
About Exa
Exa is an applied AI lab that builds a search engine and API for AI models and agents, per the company.
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