AI Solutions Lead

Team

• Hire, develop, and manage a team of AI Solutions Builders (starting small, growing with as the company grows).
• Coach the team on how to get the best output from AI coding agents, how to review generated code, and how to work with enterprise customers.
• Create the feedback loops between your team and the platform engineering team so tool improvements compound.

Product Strategy for the Solutions Practice

• You will synthesize insights from customer builds and work closely with the CTO and platform team to determine which capabilities should become first-class products.
• Define the product strategy for the solutions practice: what types of applications we build, how we scope engagements, and where we draw the line between custom builds and platform capabilities.
• Identify patterns across customer engagements that should become repeatable products or templates.
• Shape the roadmap for our AI-assisted development tooling based on what you see working (and failing) in the field.

The Playbook

• Define and evolve the playbook for AI-assisted application development: which tools to use, how to structure prompts, code review standards, quality benchmarks for AI-generated output, how to handle edge cases.
• Document what works, what doesn’t, and which approaches produce the best outcomes for different types of applications.
• Stay on top of the rapidly evolving AI tooling landscape and integrate new capabilities as they emerge where they materially improve speed or reliability.

What Success Looks Like

This is a new team, so we’re not handing you a rigid scorecard. But in 6–9 months, we’d expect to see a small high-performing team in place and consistently shipping customer applications. A documented playbook that’s being used actively, tested and refined through real engagements, and early signs of repeatable patterns: the kinds of applications and approaches that work well enough to become starting points rather than blank slates. The clearest signal of success is that customers are getting better applications, faster, and your team is getting measurably better at building them.

What We’re Looking For

Must Have

• Product management or customer facing product engineering background in adtech or martech. You understand how consumer brands think about audience targeting, campaign optimization, and growth — because you’ve built products in that world.
• Technical depth. You can read Python, React, and SQL. You understand API design, data modeling, and system architecture. You’re the PM who engineers actually want to work with because you speak their language.
• Hands-on experience building real applications using AI coding tools (Claude Code, Cursor, Copilot, or similar). You’ve built real things with them and have strong opinions about how to get the best results. You understand both their leverage and their limitations.
• Experience leading small teams in a startup or high-growth environment. You know how to hire well, set a high bar, and develop people — without layers of process.
• Enterprise customer-facing experience. You’re comfortable in a room with a CMO and equally comfortable debugging a failing build.
• A bias toward doing the work yourself, not delegating it. You want to build, not just manage.

Nice to Have

• Experience building or scaling a solutions engineering, professional services, or technical consulting practice.
• Familiarity with synthetic data, audience enrichment, or data clean rooms.
• Background in programmatic advertising, DMP/CDP platforms, or marketing analytics.
• You’ve defined the process and methodology for a team that didn’t exist before you joined.

How to Apply

Show us how you think about building with AI. A project you’ve shipped, a framework you’ve developed for your team, a writeup of your approach — anything that shows you’re already doing this work and thinking about how to make others better at it.