PythonExperience: 8+ years, senior candidates only | Type: Full-time | Location: Remote (India)
WHAT WE'RE BUILDING [See www.juliet.space]
We're building Juliet — an AI that runs marketing end to end. Our users are marketers, founders and CEOs, growth leads, agencies, fractional execs, 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 it's a browser-based studio backed by cloud sandboxes, a real-time streaming pipeline, and a LangGraph agent working across 83
tools and 63 skill modules.
Small team, big ambitions. You'll ship things users touch daily — not write JIRA tickets about them.
THE ROLE
We're hiring one architect-level engineer to lead Juliet's technical execution. You'll own the architecture end to end, set direction for a small dev team,
review what ships, and still write code every day — a player-coach, working directly with the founder. Your calls on agent behavior, system design, and
infrastructure become the product.
THE STACK
- Frontend: Next.js 15, React 19, TypeScript, Zustand, CodeMirror 6, XTerm.js
- API: NestJS 11, Supabase, Redis, PostgreSQL, Server-Sent Events
- AI agent: Python 3.11, LangGraph 1.x + LangChain, Anthropic / Google / OpenAI model providers
- Infra: Modal cloud sandboxes, Docker, Netlify deployments
- Monorepo: Turborepo, pnpm
WHAT YOU'LL WORK ON
→ Agentic AI workflows — design and improve multi-step LangGraph agent graphs that plan, write code, run commands, and self-correct across 83 tools, 63 skill
modules, and a 30+ stage middleware pipeline
→ Real-time streaming architecture — SSE pipelines that relay agent actions from the Python backend through NestJS to the browser, with reconnection, health
monitoring, and interrupt handling (plan approvals, clarifying questions)
→ Agent execution environments — sandbox lifecycle management on Modal: file sync, terminal I/O, command execution, and live preview with per-asset esbuild
bundling
→ State & memory systems — LangGraph Postgres checkpointers, middleware-injected context (goals, design docs, memory anchors), and conversation summarization
→ Marketing intelligence & asset engines — public AEO, CRO, and brand-perception audit pipelines (multi-engine LLM probing, streamed reports) and a 20-format
marketing asset studio
→ Full-stack product features — collaboration (roles & permissions), the Netlify deployment pipeline, image handling, subscription/quota systems, and onboarding
flows
WHAT WE'RE LOOKING FOR
Must-have:
- 8+ years of professional software engineering experience — this is a senior seat; most people who thrive in it have closer to ten, including time as a tech
lead or architect who owned a system end to end
- Both worlds on your resume: engineering rigor inside a large company AND 0→1 ownership at an early-stage startup
- Strong TypeScript and Python — you're comfortable shipping production code in both
- Production agentic experience — you've built and operated LLM agent systems with LangGraph, LangChain, or similar: agent graphs, tool use, state management,
prompt engineering, evals. Well beyond calling a chat endpoint.
- AI-native development workflow — you drive Claude Code and Codex proficiently as everyday tools: agentic loops, skills, parallel sessions. You have opinions
about working with coding agents because you do it daily.
- Experience with React/Next.js and building complex, stateful UIs
- Familiarity with real-time systems (SSE, WebSockets, streaming APIs)
- Architect-level system design — you can own how data flows across four services and defend every tradeoff
Strong plus:
- Background in developer tools, IDEs, or coding platforms
- Container runtimes and sandboxed execution (Modal, E2B, Firecracker)
- NestJS or similar Node.js backend framework experience
- Depth in PostgreSQL, Redis, and Supabase
- LLM observability and evals (LangSmith or similar)
- Exposure to marketing or growth products
WHY THIS ROLE IS DIFFERENT
You set the architecture. This isn't a feature factory, and you won't be implementing someone else's design doc. Technical execution is yours to lead.
AI-native from day one. The agent isn't bolted on — she IS the product. You'll work at the intersection of LLM capabilities, agent infrastructure, and product
design for non-technical users.
Depth over breadth. The system spans cloud sandboxes, streaming infrastructure, agentic AI, and full-stack web — an IDE's internals where the only developer is
the AI. There's always a hard problem to solve.
Small team, big leverage. Your code ships fast and reaches users immediately. No layers of approval.
HOW TO APPLY
Send us:
1. Your resume
2. A short note on the most complex agentic system you've shipped — what broke, and what you'd redo
3. Your AI coding setup: which tools you drive daily (Claude Code, Codex, other) and how
4. (Optional) A link to something you've built that you're proud of
Shubham Vishwakarma
Full Stack Developer - Averlon
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Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon