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 buildingWe'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 roleYou will own the technical direction and delivery of an initiative. You're an excellent product engineer who ships code alongside your team — expect to spend 50–60% of your time in code.
This is not a management role. You write production code, review PRs, make architecture calls, and set the engineering standard your team rallies around. Your leadership is rooted in technical credibility and active presence in the codebase.
You run 1:1s with engineers on your initiative — focused on the work: delivery, unblocking, and direction. You're involved in hiring because you define how your initiative is staffed: deciding who you need and joining interview loops.
Your unit of ownership: the technical quality, velocity, and direction of your initiative.
Where the line is: career growth, performance management, and the development of engineers sit with the Engineering Manager — not with the Tech Lead. Candidates should be clear on this distinction before applying.
We're a growing product team scaling into multiple initiatives, each with a lead, engineers, a design engineer, and an AI-focused engineer. You'll lead one of these initiatives.
What we're looking forThese are non-negotiables—the things we'll specifically evaluate you on:
Shipping and ownership. You've repeatedly taken ambiguous problems to production with measurable outcomes. You own features end-to-end: UI → API → data → deployment.
Technical leadership. You make architecture decisions, manage tech-debt tradeoffs, and set quality standards. Your team trusts your judgment because you're in the codebase with them.
AI/ML product literacy. You have hands-on experience building with LLMs, retrieval systems, or evaluation harnesses. You understand the primitives well enough to make sound build-vs-integrate decisions and catch failure modes.
Product judgment. You can define MVP scope, pick the right metric to move, and kill work that isn't delivering value. You connect technical choices to customer outcomes, not just technical elegance.
Initiative ownership. You define what your initiative needs — who to hire, how to structure the work, what to build and in what order — and you hold yourself accountable for delivery and outcomes.
Judgment under pressure. You'll decide build-vs-buy, choose between shipping fast and shipping right, and make architecture calls that affect the whole platform — often without complete information.
Have a strong sense for security and reliability in production AI systems.
Have excellent UI/UX taste and an eye for detail.
Be a full-stack generalist who thrives on new problems and ambiguity.
Have designed and operated distributed systems at scale.
Have deep applied LLM experience — evaluation design, prompt engineering, safety controls, and cost optimization in production.
You're looking for a people management role — see Engineering Manager.
You want to step back from hands-on engineering.
TypeScript-first
React and Fastify
Postgres, GCP
Latest AI primitives
Initiative-driven. Organize around customer problems, not org charts.
Customer-obsessed. Features that don't move customer metrics get cut.
Iterative by default. Ship small, learn fast, fix forward.
AI-first leverage. If a tool can do it, a person shouldn't.
Direct feedback. Actionable feedback, given immediately.
Autonomy with accountability. Outcomes over process.
Unlimited AI budget.
Autonomy to do your best work.
A real AI product with real customers.
A sharp, motivated team that values ownership and candor.
Competitive compensation with a meaningful equity component.
Hiring manager screen (30 min).
Remote task (async, time-boxed, ~1 hour) — a realistic technical leadership exercise.
Technical interview (Prague, ~1 hour).
On-site trial day (2 days), fully compensated.
