Duvo Inc - AI Platform Engineer

• 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. • Experience building production AI systems. • Shipping and ownership. You've taken ambiguous AI problems to production with measurable outcomes. You own the full lifecycle — build, evaluate, deploy, monitor, iterate. • Shipping and ownership. • 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. • System design for AI. • 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. • Evaluation design. • 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. • Debugging and diagnosis. • 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. • Judgment as AI evolves. • 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. • TypeScript-first (our agent runtime is TypeScript) • 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. • Initiative-driven. • Customer-obsessed. We solve real problems, not hypothetical ones. Features that don't move customer metrics get cut. • Customer-obsessed. • Iterative by default. We ship small, learn fast, and never get attached to yesterday's code. This means things break sometimes — we fix forward. • Iterative by default. • 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. • AI-first leverage. • Direct feedback. We give each other actionable feedback immediately. This can feel uncomfortable — we think that's worth it. • Direct feedback. • Autonomy with accountability. We trust people to make decisions and hold them to outcomes, not process. • Autonomy with accountability.