QuoteWell

Insurance

AppliedAIEngineer

$95–135k ~AI est. Austin, Texas, United States FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid+ candidates.

The Brief

“Applied AI Engineer at QuoteWell. Embed at retail agency. Own customer-side execution”

Industry & Context.

Insurance
Problems you'll solve

Troubleshoot; Scope; Reason

Eligibility Requirements

Travel ~25% of the time

What They're Looking For.

Must Have

1-5 years shipping production software, Engineering foundation

Nice to Have

Python a plus, TypeScript a plus, Talented dropouts welcome

What You'll Do.

Embed at retail agency

Own customer-side execution

Build governed AI workflows

Integrate with carrier portals

Identify agency operations

Design review patterns

Design attribution patterns

Design override patterns

Carry field signal back

Represent QuoteWell with customers

Scope problems out loud

Troubleshoot problems out loud

Reason about problems out loud

Full Job Description

About QuoteWell We started as a vertical SaaS company in insurance. We grew into operating our own wholesale brokerage and a programs group. Through that, we've lived the operational reality of applying AI in insurance — what works in production, what breaks, what compounds. We've just closed fresh funding to scale this operating model to retail insurance agencies across the country. Our CEO, Joey Bouchard, built and ran applied AI teams at Palantir. We bring that technical rigor together with years of hands-on insurance operations experience to help agencies deploy AI that works in the real world. (Why insurance is worth this attention. https://medium.com/@jbouchard_6468/why-insurance-is-awesome-e5a00daa752b) Why this role exists Brokerage execution runs on heroics. Service quality depends on who owns the account, how overloaded they are, and what they happen to remember at the right moment. A submission gets marketed broadly, or it doesn't. A renewal gets the same rigor as new business, or it doesn't. A missing detail gets chased, or it stalls. We don't think that's inevitable. Brokerage execution can be made systematic: software, automation, and AI carrying disciplined follow-through alongside human experts, inside clear bounds, in a regulated environment. The hard part isn't the model call. It's governability — making AI accountable enough to trust with real deal flow. Who is the AI acting for? What is it allowed to do? What happens when it's wrong? In a regulated industry, those aren't side questions. They're the engineering problem. We've been answering them inside our own wholesale brokerage for years. That's where we test whether the model actually produces more reliable, scalable, and consistent service. If it doesn't work for us, it doesn't deserve to be packaged for the market. Now we're packaging it. The role Applied AI Engineers sit at the point where Terminal, our platform, meets the messy reality of a customer's agency. You own the outcome at the cust

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