AI Product Engineer - End-to-End Ownership

Who we are

Enterprise teams still copy data between systems all day. Work gets stuck in emails, legacy UIs, and handoffs. That chaos is costly, slow, and risky.

We're a fast-moving team on a mission to end it for good. Traction is strong and we're solving real problems for real customers, but to win, we need exceptional talent. We stay humble, do the work, and let results speak.

What we are building

We're building the AI operations platform for retail and CPG enterprises—a horizontal platform where AI agents execute end-to-end work across UIs and APIs with governance built in.

Where copilots stop, Duvo finishes the job. Business users specify the outcome; agents plan, act, request approvals on exceptions, and learn with every run. We start with a retail wedge (category management, supply chain, finance ops) where ROI is obvious, then expand to adjacent functions and sectors.

Velocity is our moat: ship fast, iterate faster, compound learning.

The role

You will own end-to-end product features—from user-facing UI to API to data to deployment. You're the kind of engineer who connects technical choices to customer outcomes and ships with high velocity under ambiguity.

Your unit of ownership: user-facing features and the systems behind them, delivered to production and measured against customer impact.

What we're looking for

These are non-negotiables. The things we'll specifically evaluate you on:

• Shipping and ownership. You've repeatedly taken ambiguous requirements to production. You own the full stack of a feature (UI, API, data, deployment) and you don't wait for someone to tell you what to build next.
• Product judgment. You can define MVP scope, pick the right metric to move, and kill work that isn't delivering value. You think about what the user needs, not just what's technically interesting.
• AI comfort. You've worked alongside AI systems—at work or in side projects. You're comfortable building features that interact with LLM outputs (parsing agent responses, designing human-in-the-loop flows), but you won't be training models.
• Strong sense of product quality. You care about the details of how a feature looks and feels, not just whether it works. You notice when something is off and you fix it.
• Collaboration. You're low-ego and team-first. You give and receive direct and constructive feedback, align proactively with product and design, and unblock yourself and others.
• Judgment in a fast-moving environment. You'll often define your own scope based on customer problems surfaced through product feedback and competitive gaps — then ship it within a week.

• Have a strong sense for security and reliability in production systems.
• Have scalable, distributed-system instincts—you've designed and operated systems that scale.
• Have deep applied LLM experience—evaluation design, prompt engineering, safety controls, and cost optimization in production.
• Have experience designing interfaces for AI-assisted workflows — confidence signals, human-in-the-loop interactions.

• You need significant hand-holding or aren't energized by figuring things out yourself.
• You primarily want to work on infrastructure (see our SRE and AI Platform Engineer roles).

• Type Script-first:
  Next.js, React, Tailwind, Fastify, Kysely (Postgre
  
  SQL), Zod
• GCP
• Latest AI primitives

• Initiative-driven. We organize around customer problems, not org charts. Problems surface through product feedback, competitive analysis, and direct customer conversations — then we prioritize, build, and ship weekly.
• Customer-obsessed. We solve real problems, not hypothetical ones. Features that don't move customer metrics get cut.
• Iterative by default. We ship small, learn fast, and never get attached to yesterday's code. This means things break sometimes — we fix forward.
• AI-first leverage. We use AI to move faster and focus human time where it matters most. If a tool can do it, a person shouldn't.
• Direct feedback. We give each other actionable feedback immediately. This can feel uncomfortable — we…