Lead Full Stack Software Engineer

We are seeking a Lead Full Stack Software Engineer to build intuitive, performant user interfaces and frontend integration libraries that make GenAI and intelligent automation accessible and useful across IT, HR, Finance, Sales, Marketing, and Customer Support.

Design and build end-to-end GenAI-powered applications, emphasizing intuitive web-based UIs and performant client-side API integrations Develop responsive, accessible frontend interfaces engineered for streaming responses and low latency Build and maintain a library of reusable frontend components, hooks, and design systems for enterprise GenAI applications Implement LLM-based experiences using frameworks like LangChain or LlamaIndex with OpenAI, Claude, or Gemini Collaborate with designers and product managers to iterate rapidly on novel AI UX patterns such as chat assistants, prompt builders, and feedback loops Ensure security, accessibility, observability, and test coverage across client-side and full-stack implementations Create responsive, accessible, and high-performance web UIs tailored for AI-driven interaction patterns like streaming text, prompt inputs, citations, and summaries Maintain a shared library of reusable frontend components and hooks that enable internal teams to drop GenAI capabilities into their applications Deliver UI components that provide transparency, observability, and confidence in AI-generated outputs 5+ years of production experience with React, TypeScript, or Vue for enterprise web applications 5–8 years of software engineering experience across the full web stack, including frontend UIs, REST APIs, and microservices 2+ years of experience integrating GenAI features, LLM APIs, and prompt UI patterns into web applications Strong Python backend development skills and 2+ years of experience consuming and building cloud microservices on AWS, Docker, and REST APIs Proven experience building, documenting, and maintaining reusable component libraries, design systems, and custom hooks Proficiency in English at a B2+ level Familiarity with real-time streaming UX patterns such as SSE/WebSockets, prompt builders, citations, and summary UI components Knowledge of WCAG 2.1 AA compliance and accessible UI component architecture Skills in client-side or Node.js LangChain, LangGraph, or LlamaIndex orchestration Familiarity with RAG pipelines, vector search tools like Pinecone or OpenSearch, and embedding concepts Background in AI evaluation tooling, feedback loops, and guardrails in user interfaces