Engineering Manager

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.

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.

You will own the people, processes, and partnerships that let an engineering team ship great product. You won't be writing code day-to-day — your job is to build a team that doesn't need you in the codebase, and to create the conditions where they do their best work.

You'll be our first Engineering Manager, building the engineering management function from scratch. There are no existing processes to inherit — you'll create the hiring pipeline, team structure, career frameworks, and rituals that let us scale from a small team to a multi-initiative organization.

Your unit of ownership: team performance, hiring pipeline, career growth of your reports, and cross-functional alignment with product, design, and external partners.

We're a growing product team scaling into multiple initiatives. You'll work alongside Tech Lead Managers who own technical direction — your focus is people, process, and cross-team execution as the org grows.

In your first 90 days, you'll establish the interview process and hiring pipeline, define team structure for the first initiatives, run 1:1s with existing engineers to understand their growth goals, and begin building the frameworks that will let the team double.

These are non-negotiables—the things we'll specifically evaluate you on:

• Hiring and team building. You own the pipeline end-to-end: sourcing, interviewing, closing. You've built teams before and have a track record of attracting strong engineers in competitive markets.

• Career development. You run meaningful 1:1s, give continuous feedback, and help each engineer chart a growth path. You know when to coach, when to stretch, and when to shield.

• Stakeholder management. You work closely with product, design, and leadership to align priorities, set expectations, and keep communication flowing. You translate between technical reality and business needs.

• Team health and execution. You shape how the team works — rituals, processes, norms — so delivery stays sustainable and morale stays high. You remove blockers, manage dependencies, and ensure commitments are met without micromanaging technical decisions.

• Customer focus. You instill customer obsession in your team — ensuring their efforts are always aligned with solving real problems, not building for the sake of building.

• Building from scratch. You'll make calls on hiring priorities, team structure, and process when there's no playbook — we're building the engineering org, not maintaining one.

• Have an engineering background yourself, giving you credibility and context when supporting technical teams.

• Have experience partnering with external vendors, infrastructure providers, or AI model providers.

• Be skilled at facilitating cross-team alignment in a fast-growing organization.

• Have exposure to AI/ML products — not required, but helpful context for the problems your team solves.

• You want to write code day-to-day — see Tech Lead.

• You're looking for an established org with existing processes and frameworks.

• TypeScript-first

• React and Fastify

• Postgres, GCP

• Latest AI primitives

These are real tradeoffs we've made, not aspirations:

• 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 think that's worth it.

• Autonomy with accountability. We trust people to make decisions and hold them to outcomes, not process.

• Unlimited AI budget. We don't just allow AI tools — we strongly encourage them. Want to try a new tool? Buy it. Want to automate part of your workflow? Do it.

• Autonomy to do your best work. Want to meet someone to learn from? Set it up. Want a mentor? Go get one. Want to fly out to talk to an important customer? Just ask.

• A real AI product with real customers. You're not building demos or internal tools. Enterprise customers use what you ship, and their feedback drives what you build next.

• A sharp, motivated team that values ownership and candor.

• Competitive compensation with a meaningful equity component. You can trade salary for additional equity if you prefer more upside.