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# AI Engineer, Agent Infrastructure

- **Company:** ZED
- **Location:** San Francisco, CA, United States
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Software Debugging, Distributed Systems, Design of User Interfaces, Human-Computer Interaction, Workflow Management Systems, Large Language Models, Prompt Engineering, Backend, Agentic-AI, Web Api
- **Published:** November 10, 2026
- **Apply:** https://www.careerbuilder.com/job-details/ai-engineer-agent-infrastructure-san-francisco-ca--a6e2a68a-c6ad-48dc-b076-cdf0f87de3c6

## About the Role

Artificial Intelligence (AI), Financial Services, Banking Services, Artificial Intelligence (AI), Facebook, Startup, Business-to-Business (B2B), Artificial Intelligence (AI) Agents, Technical Recruiting, User Interface/Experience (UI/UX), * Direct experience shipping production LLM or agent systems end-to-end - orchestration, evaluation, reliability, not just prototypes

 * Strong backend or infrastructure engineering foundation (distributed systems, APIs, platform engineering)
 * Experience with workflow orchestration, automation systems, or agent frameworks
 * Familiarity with evaluation and observability loops for AI systems
 * Ability to think across both infrastructure concerns and product behavior - this role requires both

Strong Signals

 * You've built agent systems that take real actions, not just generate text
 * You've designed execution environments - task runners, sandboxes, job systems
 * You've worked on AI that's deeply embedded in a real product, not a side project or internal tool
 * You have experience with observability and evaluation loops for AI systems in production

## Description

We're hiring an engineer to own the infrastructure layer behind our production AI agents.

This is not a prompt engineering role. It's not a UI role. It's about the harness around LLMs - the systems that determine how agents actually execute tasks, interact with tools, access internal and external systems, stay within permission boundaries, and behave reliably in production.

You'll sit at the intersection of backend infrastructure and product, and what you build will define how AI is deployed across the company., * Build and own the execution layer for AI agents - task orchestration, tool calling, state management

 * Define how agents interact with internal systems and external APIs
 * Design sandboxed environments and permissioning models for safe, controlled agent execution
 * Build evaluation, monitoring, and debugging infrastructure for agent behavior in production
 * Integrate agents into real product workflows where correctness and reliability are non-negotiable
 * Improve system performance across latency, cost, and quality tradeoffs