Chief Full Stack Software Engineer

We are building GenAI-powered web experiences with intuitive UI and low-latency integrations to bring automation into everyday enterprise workflows. As a Chief Full Stack Software Engineer , you will lead frontend UX patterns, reusable component libraries, and full-stack delivery across React/TypeScript/Vue and Python microservices on AWS. Apply now to help scale trusted AI experiences across the organization

Architect and deliver end-to-end GenAI-powered applications with intuitive web UIs and fast client-side API integrations Engineer responsive, accessible frontend experiences optimized for streaming responses and low latency Develop and evolve reusable frontend components, custom hooks, and design systems for enterprise GenAI applications Implement LLM-driven features using frameworks like LangChain or LlamaIndex with OpenAI, Claude, or Gemini Partner with designers and product managers to iterate quickly on AI UX patterns including chat assistants, prompt builders, and feedback loops Enforce security, accessibility, observability, and test coverage across client-side and full-stack work Create high-performance, accessible web UIs for AI interaction patterns such as streaming text, prompt inputs, citations, and summaries Maintain shared component and hook libraries so internal teams can embed GenAI capabilities into their applications Deliver UI components that increase transparency, observability, and user confidence in AI-generated outputs Proven track record with React, TypeScript, or Vue in production for enterprise web applications (7+ years) Extensive full-stack engineering experience delivering frontend UIs, REST APIs, and microservices (7+ years) Hands-on background integrating GenAI features, LLM APIs, and prompt UI patterns into web applications (3+ years) Advanced Python backend development skills, including building and consuming cloud microservices on AWS with Docker and REST APIs (3+ years) Demonstrated ability to build, document, and maintain reusable component libraries, design systems, and custom hooks English proficiency at B2+ (Upper-Intermediate) level Working knowledge of GraphQL for client-server data interactions Practical experience with LangChain or LlamaIndex in JavaScript/Node.js contexts Familiarity with Model Context Protocol concepts and integration considerations Exposure to React Native for shared UI patterns across web and mobile surfaces Understanding of vector databases and embedding concepts used in RAG-style applications 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