Founding AI Engineer

Provn is helping Envorso Sports recruit a Founding AI Engineer. Please apply on our home page on Provn and take the challenge to stand-out from competition.

About Envorso Sports

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Envorso Sports is building the platform that runs a professional sports team's entire fan-facing business: ticketing, website, mobile app, and increasingly commerce, CRM, and marketing, from one system instead of six disconnected vendors.

Today, most teams run a fragmented stack: a website vendor, a ticketing provider, a separate mobile app, CRM tools, marketing automation, and game-day engagement solutions bolted together. Teams don't want to pay five vendors and contractors to stitch the data together. That creates a disconnected fan journey and leaves revenue on the table.

Our platform is live today with the Seattle Seawolves of Major League Rugby, and the league itself is now on the platform, with individual teams onboarding through the season. Ticketing, website, and mobile app are shipped and in production with paying customers. CRM, marketing automation, personalization, and revenue operations are what we build next, AI-native from the start.

We want candidates to know exactly where the product stands before they join.

About the Founding Engineer Role

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You'll be our first in-house engineer. This is a founding role: you'll own the fan data platform for the long term and help shape how we grow engineering, not ship one feature and move on.

The problem is simple to state. Our teams and the league sit on a large fan database: ticket transactions, web and app activity, shop purchases, and email engagement. Today almost none of it is used.

One salesperson calls season-pass holders; one marketer sends a broadcast email and sees who bites. Your job is to turn that data into revenue: unify what we know about each fan across ticketing, web, app, shop, and email; decide which fans should get which offer; and deliver it through email and in-app offers or push.

Offers are concrete: a promo code for an abandoned cart, a season pass or match pass, a merch bundle, a seat upgrade. Getting the wrong offer to the wrong fan costs real money and real trust with a small, loyal fan base.

You'll build for a three-to-five-year horizon, not just this quarter. We need someone who thinks like an architect (data model, scale, failure handling, consent, cost) while still shipping something a team can use in weeks.

You'll also be the engineer closest to our customers' front offices. Part of the job is understanding how a team's revenue and marketing operations actually work, and helping decide what we build first based on impact and effort.

You'll report to Gopal Rao and work alongside our five-engineer team in Argentina as one small group. Expect real overlap-hours collaboration, and expect to take a problem, make the call, and ship without waiting on someone else.

What You'll Do

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• Own the fan data platform end to end: a unified fan record across ticketing, web, app, and email, designed to hold up as we grow from one team's list to a whole league's
• Ship fan segmentation and a targeted offers engine that decides which fans get which offer, and delivers it by email reactivation and in-app offers or push
• Build marketing ops and revenue ops driven by agentic AI: fan identity, segments, campaign builder and automation, and propensity, churn, and pricing models
• Bring commerce into the fan record: shop purchases alongside tickets, so offers can cross-sell merch, passes, and seat upgrades
• Build the surfaces people use, in TypeScript and Next.js on web and in our mobile app, so offers are something a marketer reviews and a fan acts on, not just an API response
• Design for production from day one: consent and opt-in enforcement, no duplicate sends, failures that are visible instead of silent, and costs you can explain
• Work with team front offices to understand their revenue goals, and sequence the roadmap by impact and effort
• Make AI how we ship: build an AI-native product where models change the outcome for a marketer or a fan, and run an agentic development process (AI-SDLC) with coding agents, specs and tests as guardrails, and human review before anything reaches a fan
• Help decide how engineering grows: how we scale with our Argentina team, what we build in-house, and who we hire next

How We Hire

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Apply through Provn by completing a short challenge and video walkthrough. If your submission clears the bar, you'll move straight to a conversation with our team. We don't wait to batch candidates.

The process: a screen with Gopal, a technical interview, then a panel with engineering and product leaders.

Work Authorization

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Must be authorized to work in the US. No employer-sponsored visas for this role.