Design Engineer (EU/UK Based - Remote)

AI Platform Engineer (EU/UK Based - Remote)

Duvo

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 the AI infrastructure that makes our agents reliable, fast, and safe in production. You build the agent runtime, evaluation pipelines, context management systems, tool orchestration, and the observability tooling that lets agents execute end-to-end work for enterprise customers. This is applied AI systems engineering, not ML research. You ship production systems that use LLMs, retrieval, and agent orchestration—and you're accountable for their reliability, cost, and quality in the real world. Your unit of ownership: the AI platform layer — agent runtime, context management, tool execution, evaluation harnesses, and prompt engineering for system behaviors. You own sandbox behavior and agent runtime logic; SRE owns sandbox infrastructure and capacity. We're a growing product team scaling into multiple initiatives, each with a lead, engineers, a design engineer, and an AI-focused engineer. WHAT WE'RE LOOKING FOR These are non-negotiables—the things we'll specifically evaluate you on: * Experience building production AI systems. Not research — making LLMs reliable at scale. You've dealt with prompt engineering, context management, tool use, or agent orchestration in production. * Shipping and ownership. You've taken ambiguous AI problems to production with measurable outcomes. You own the full lifecycle — build, evaluate, deploy, monitor, iterate. * System design for AI. You can design systems that handle the unique challenges of AI: non-deterministic outputs, context window limits, tool execution failures, and cost optimization. You think about reliability, cost, and latency as first-class concerns. * Evaluation design. You can build evaluation frameworks that catch regressions, measure quality, and give the team confidence to ship AI features. You understand failure taxonomies and know how to create meaningful test sets. * Debugging and diagnosis. You're hypothesis-driven when things break. You can trace failures across model behavior, data pipelines, and infrastructure to find root causes. * Judgment as AI evolves. You'll make build-vs-integrate decisions on AI infrastructure with incomplete benchmarks, and course-correct fast as models and providers evolve. YOU MIGHT ALSO * Have scalable, distributed-system instincts—you've designed and operated systems that handle high throughput and complex failure modes. * Have a strong sense for security in AI systems—prompt injection, insecure output handling, supply chain risks. * Have contributed to open-source AI tooling or infrastructure projects. THIS IS NOT FOR YOU IF * You want an ML research role — we don't train models. * You primarily want to build user-facing product features and UI. OUR TECH STACK * TypeScript-first (our agent runtime is TypeScript) * Postgres, GCP * Latest AI primitives HOW WE WORK 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. WHAT WE OFFER * 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. * Compensation 250.000,- CZK / month with a meaningful equity component. You can trade salary for additional equity if you prefer more upside. HOW WE HIRE We respect your time and aim to move fast: 1. Discovery call with a senior teammember (30 min). We'll talk about you, how you think and whether there's mutual fit. 2. Remote task (async, time-boxed, ~1 hour). Build a small product end-to-end. Not LeetCode. 3. Technical interview (online, ~1 hour). Meet the team. We'll go deeper on your experience, system design, product thinking, and collaboration. No trick questions — we want to see how you think and build. 4. On-site trial day (2 days). Ship something small to production with us and see how we work together. Fully compensated.

Site Reliability Engineer (EU/UK Based - Remote)

Duvo

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 the reliability, security, and infrastructure that lets our platform run AI agents for enterprise customers. This isn't traditional web app SRE — our agents execute arbitrary code in sandboxes, make unpredictable external API calls, and run for hours. Keeping this reliable, secure, and observable is the job. You'll be part of newly formed SRE team as one of the first teammembers. Infrastructure is currently owned collectively by product engineers — you'll take ownership, inherit real infrastructure (25+ Terraform modules, full OpenTelemetry pipeline, Prometheus/Grafana monitoring), and build the reliability practice from scratch. Your unit of ownership: platform reliability, infrastructure, observability, and incident response. You own sandbox infrastructure and capacity; the AI Platform Engineer owns sandbox behavior and runtime logic. We're a growing product team scaling into multiple initiatives, each with a lead, engineers, a design engineer, and an AI-focused engineer. WHAT WE'RE LOOKING FOR These are non-negotiables—the things we'll specifically evaluate you on: * Distributed systems experience. You've designed and operated systems that scale. You understand failure modes, capacity planning, and the tradeoffs between consistency, availability, and latency in real production environments. * Security mindset. You'll handle enterprise data flowing through sandboxed environments, manage KMS encryption, configure Cloud Armor WAF rules, and ensure network isolation between tenant workloads. Security is a default consideration, not an afterthought. * Observability and incident response. You build monitoring and alerting that catches problems before customers do. When incidents happen, you lead structured responses, find root causes, and drive lasting fixes — not just restarts. * Infrastructure as code and automation. You automate everything you can. You've worked with IaC tools, CI/CD pipelines, and container orchestration in production. Manual runbooks make you uncomfortable. * Shipping and ownership. You don't just maintain systems — you improve them. You take ownership of reliability projects from proposal to production, and you measure the results. * Judgment on where to invest. You'll decide what to automate first, where to invest in reliability vs. ship speed, and make incident calls with incomplete information. YOU MIGHT ALSO * Have experience with GCP, Kubernetes, or similar cloud-native infrastructure. * Have worked with sandboxed execution environments or multi-tenant isolation. * Be comfortable with AI/ML production systems — understanding the unique reliability challenges of LLM-based applications. * Have a product engineering background — you've built features and understand the developer experience you're supporting. THIS IS NOT FOR YOU IF * You want a traditional ops role where you follow runbooks — we're building the reliability practice, not maintaining one. * You want to build AI features — see AI Platform Engineer. OUR TECH STACK * GCP (Cloud Run, GKE, GCS) * Terraform, Docker * Prometheus, Grafana, Loki, OpenTelemetry * TypeScript and Python services (you'll read and occasionally modify application code, but deep language expertise isn't required) * Postgres, Redis HOW WE WORK 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. WHAT WE OFFER * 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. * Compensation 250.000,- CZK / month with a meaningful equity component. You can trade salary for additional equity if you prefer more upside. HOW WE HIRE We respect your time and aim to move fast: 1. Discovery call with a senior teammember (online, 30 min). We'll talk about you, how you think and whether there's mutual fit. 2. Remote task (async, time-boxed, ~1 hour). Build a small product end-to-end. Not LeetCode. 3. Technical interview (online, ~1 hour). Meet the team. We'll go deeper on your experience, system design, product thinking, and collaboration. No trick questions — we want to see how you think and build. 4. On-site trial day (2 days). Ship something small to production with us and see how we work together. Fully compensated.

Product Engineer (EU/UK Based - Remote)

Duvo

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. YOU MIGHT ALSO * 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. THIS IS NOT FOR YOU IF * 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). OUR TECH STACK * TypeScript-first: Next.js, React, Tailwind, Fastify, Kysely (PostgreSQL), Zod * GCP * Latest AI primitives You don't need to know all of these, but you should be excited to learn them quickly. HOW WE WORK 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. WHAT WE OFFER * 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. * Compensation 250.000,- CZK / month with a meaningful equity component. You can trade salary for additional equity if you prefer more upside. HOW WE HIRE We respect your time and aim to move fast: 1. Discovery call with a senior teammember (online, 30 min). We'll talk about you, how you think and whether there's mutual fit. 2. Remote task (async, time-boxed, ~1 hour). Build a small product end-to-end. Not LeetCode. 3. Technical interview (online, ~1 hour). Meet the team. We'll go deeper on your experience, system design, product thinking, and collaboration. No trick questions — we want to see how you think and build. 4. On-site trial day (2 days). Ship something small to production with us and see how we work together. Fully compensated.