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# Founding Infrastructure Engineer

- **Company:** AI STRUCTURED LLC
- **Location:** New York, NY, United States
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Amazon Web Services, Computer Vision, Microsoft Azure, Cloud Computing, Continuous Integration, Linux, Github, Python (Programming Language), PostgreSQL, Redis, OpenAI, Search Technologies, Data Streaming, Large Language Models, Claude Code, AI Coding Agents, Agentic-AI, Fastapi, Bicep, Claude, Google Gemini, Agent SDK, Container Apps
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25303_5d790632e4e321bd66db5c479b7fde57

## About the Role

* You've run cloud infrastructure in production for at least three years. Azure, AWS, or GCP; Azure is a plus, not a requirement.
 * You've isolated workloads in production. Containers, microVMs, or Linux sandboxing in a system with real users. We'll go deep on this in the interview.
 * Deep production container experience: Container Apps, ECS, AKS, EKS, or similar.
 * You've owned infrastructure as code and CI/CD end to end.
 * You've run Postgres and Redis in production and been on call for them.
 * You use AI coding agents in your own work and check what they produce. We use Claude Code and Codex daily.
 * You're in New York or willing to move here. We're in the office in the Financial District five days a week.

## Description

* The LLM traffic layer: multi-tenant, metered, every token accounted for in customer billing.
 * Streaming for chat and progress updates that survives deploys and dropped connections.
 * The document pipeline: rendering, OCR, extraction on very large PDFs.
 * CI/CD, the self-hosted runner fleet, and incident response.

You'd also ship in the codebase (Python, FastAPI) alongside the product engineers.

We're on Azure: Container Apps with KEDA-scaled jobs, Bicep, GitHub Actions, Postgres, Redis, Front Door, Entra ID. Models from Anthropic, Gemini, and OpenAI. We're hiring for depth in containers and isolation, not for any one tool. If you'd run part of it differently, you'll be the one deciding., * Infrastructure for AI agents specifically: sandboxes for generated code, agent orchestration, inference fleets, vector search.

 * KEDA or other event-driven autoscaling.
 * Agent harnesses: MCP, tool-use loops, the Claude Agent SDK, LangGraph.
 * Document processing, computer vision, or time spent in AEC.
 * Python beyond scripts. Our deploy tooling is Python.