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from problem framing and architecture through implementation, deployment, and production operations\nDesign, build, and ship full-stack systems: frontend, backend APIs, data layers, and AI/ML services - not just models or prompts\nTake a product from idea to production independently: design APIs, build UIs, wire data flows, integrate models, and operate the system\nBuild and productionize LLM / GenAI features (RAG, agents, tool-calling, evaluation, guardrails) and integrate them into real user-facing applications\nDesign scalable applicationand model-serving architectures that are reliable, observable, and cost-aware\nOwn data pipelines, retrieval, embeddings, and evaluation loops so AI features stay accurate and measurable in production\nWork across the stack as needed - React/Next (or equivalent), Node/Python/Go services, databases, queues, cloud, and CI/CD - without waiting on a specialist for every layer\nPartner with product, design, and engineering to turn ambiguous problems into shipped features\nReview code, raise the bar on system design, and mentor engineers on both AI and full-stack practices\nEstablish engineering standards for AI quality, evaluation, security, and release confidence\nDebug production issues across the full path: UI, API, infra, data, and model behavior\nRequirements\n15+ years of software engineering, with recent hands-on ownership of production systems\nProven full-stack depth: can design and implement frontend, backend, APIs, data stores, and cloud infrastructure - 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