AI-Accelerated Full Stack Engineer

AI-Accelerated Full Stack Engineer About the Role: Our client is hiring an AI-accelerated Full Stack Engineer who builds like it's their own product. You'll own features end-to-end-from frontend to backend to infra-and use AI tools to 10x speed, not cut corners. This role is ideal for engineers who think like founders, move without waiting, and thrive in fast, event-driven systems. You'll work with a lean, high-agency team that values outcomes over ceremony. What You'll Do: * Build and ship complete features using Next.js, Node.js, TypeScript, Python, etc. * Design and implement event-driven architectures that support scalable, decoupled systems. * Use AI tools like GitHub Copilot, GPT-4o, Claude, and LangChain to accelerate development, testing, and debugging. * Define, build, and iterate on MVPs without waiting for detailed specs or designs. * Own the full lifecycle: architecture, development, testing, deployment, and monitoring. * Troubleshoot and self-unblock without relying on DevOps or QA. * Move fast, break down complex problems, and make sound tradeoffs under uncertainty. What We're Looking For: * 7+ years of full-stack engineering experience. * Proven ability to deliver high-quality software from idea to production independently. * Strong fundamentals in API design, system architecture, and cloud infrastructure. * Hands-on experience designing and maintaining event-driven architectures (e.g., pub/sub, message queues, AWS EventBridge, SNS/SQS, Kafka, etc.). * Comfortable working across frontend (React/Next.js), backend (Node/Python), and infra (AWS, Docker, Terraform, CI/CD). * Startup mindset: you don't wait for Product, Design, or QA-you step in and drive. * Bias for action, clarity, and continuous learning. * Deep familiarity or strong interest in AI tools and workflows that improve engineering velocity and quality. Nice-to-Haves: * Experience with LangChain, vector databases, or RAG pipelines. * Experience building internal tooling or AI-enhanced developer workflows. * Exposure to HIPAA/PHI handling and healthcare compliance. * Experience applying machine learning concepts in production systems (e.g., model inference, feature pipelines, personalization, etc.).

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