Lead Software Engineer / Architect (Primarily backend) — Agentic AI Platform

Location: Remote --- WHAT WE'RE BUILDING See http://www.juliet.space We're building Juliet, an AI that runs marketing end to end. Our users are marketers, founders, CEOs, growth leads, agencies, and SMBs — not developers. They tell Juliet the goal. She plans, writes production code, and ships real marketing: conversion-optimized websites, launch assets, campaigns, audits, autonomously. That's the engineering problem in one line: the humans in the loop can't read code, so the agent has to get it right on her own — plan, build, self-correct, recover, ship. Under the hood: a browser-based studio backed by cloud sandboxes, a real-time SSE streaming pipeline, and a LangGraph agent working across 83 tools and 63 skill modules. The agent isn't bolted onto the product. She is the product. Small team, big ambitions. You'll ship things users touch daily, not write tickets about them. --- THE ROLE We're hiring one architect-level backend engineer to own Juliet's agentic infrastructure end to end. That means the agent graph, the execution environment, the streaming pipeline, the state and memory systems — and setting technical direction for the engineers working alongside you. This is a player-coach seat. You'll still write code every day, and your architectural calls become the product. You'll work directly with the founder. No PMs in between. Frontend is part of the system. You won't be leading it, but you'll need to understand how the agent's output reaches the browser and be able to ship full-stack features when needed. --- THE STACK AI agent (primary): Python 3.11, LangGraph 1.x + LangChain, Anthropic / Google / OpenAI model providers API (primary): NestJS 11, Supabase, Redis, PostgreSQL, Server-Sent Events Infra (primary): Modal cloud sandboxes, Docker, Netlify deployments Frontend (secondary): Next.js 15, React 19, TypeScript, Zustand, CodeMirror 6, XTerm.js Monorepo: Turborepo, pnpm --- WHAT YOU'LL WORK ON The majority of your time is here: Agentic AI workflows — Design, extend, and harden the LangGraph agent graph: multi-step planning, code generation, tool dispatch, self-correction, and recovery across 83 tools and 63 skill modules. This is the core of the product. Real-time streaming architecture — The SSE pipeline that carries every agent action from the Python backend through NestJS to the browser: event framing, reconnection, health monitoring, interrupt handling for plan approvals and clarifying questions. Agent execution environments — Sandbox lifecycle on Modal: container spin-up, file sync, terminal I/O, command execution, and live preview with per-asset esbuild bundling. The agent lives here. State and memory systems — LangGraph Postgres checkpointers, middleware-injected context (goals, design docs, memory anchors), conversation summarization. How the agent knows what it knows. Backend API and data layer — NestJS services, Supabase schema, Redis caching, quota enforcement, webhook handling. The plumbing the agent depends on. Marketing intelligence pipelines — AEO, CRO, and brand-perception audit engines: multi-LLM probing, parallel inference, streamed structured reports, result caching. Audit-at-scale infrastructure. The remaining ~25% of your time: Full-stack product features — Collaboration (roles and permissions), the Netlify deployment pipeline, subscription and quota flows, onboarding. You'll ship these end to end — backend first, frontend to close the loop. --- WHAT WE'RE LOOKING FOR Must-have: - 8+ years of professional software engineering, including meaningful time as a tech lead or systems architect who owned something end to end. Closer to ten is the norm for people who thrive here. - Both worlds on your resume: engineering rigor inside a large company and 0-to-1 ownership at an early-stage startup. - Production agentic systems experience. You've built and operated LLM agent systems in production with LangGraph, LangChain, or equivalent — agent graphs, tool use, state management, prompt engineering, evals. This means well beyond calling a chat endpoint. - Strong Python. You design and ship production Python daily. The agent codebase is yours to own. - Architect-level system design. You can own how data flows across four services, make tradeoffs under uncertainty, and defend every call. - AI-native development workflow. You drive Claude Code, Codex, or similar agentic tools as everyday instruments — not occasionally. You have opinions about working with coding agents because you do it constantly. - Real-time backend systems. You've built SSE, WebSocket, or streaming API infrastructure in production — not just consumed it. - Strong TypeScript. The API layer and most product features are in TypeScript. You're productive in it. Strong plus: - Background in developer tools, IDEs, or coding/execution platforms - Container runtimes and sandboxed execution (Modal, E2B, Firecracker, or similar) - Depth in PostgreSQL, Redis, and Supabase - LLM observability and evals tooling (LangSmith or similar) - NestJS or equivalent Node.js API framework experience - React/Next.js — enough to ship a full-stack feature without handoff - Exposure to marketing, growth, or publisher-facing products --- WHY THIS ROLE IS DIFFERENT You own the architecture. Not a feature factory. Not someone else's design doc. The technical execution of an AI product is yours to lead. The agent is the product. You're not adding AI to an existing system. You're building and operating the system that is the AI. Every architectural decision touches what Juliet can and can't do. Hard problems, always. The system spans cloud sandboxes, streaming infrastructure, multi-step agent graphs, and a full-stack web product — for non-technical users who can't course-correct a broken output. The bar is high. Small team, real leverage. Your code ships to users the same week. No layers of approval. --- HOW TO APPLY Send us: 1. A short note on the most complex agentic system you've shipped: what broke, and what you'd redo. A link to something you've built that involves agent graphs, tool use, or autonomous multi-step execution 2. What is one thing you would improve about Juliet? It could be a feature or a bug.