AI Operations Engineer

Role overview Join an internal AI Operations team that owns the agentic layer running business processes end to end. The AI Operations Engineer designs and ships AI workflows, agents, and scheduled tasks across every business unit, while contributing product feedback and code to the proprietary framework those automations run on.

Responsibilities - Work with interdisciplinary business units to identify processes that can be optimized and automated with AI - Design, implement, test, and maintain AI automation workflows and scheduled tasks that span the company - Monitor and measure automation executions, iterating on components that underperform - Provide product feedback for fixes and new features in the internal AI automation framework, contributing to source code and peer review - Run training sessions and write internal guides on applied AI topics including skill development, agent design, and prompt and context engineering - Act as a bridge between non-technical colleagues and the agentic workflow, translating needs into shipped solutions

Requirements - Hands-on experience building applied AI systems with fluency in prompt engineering, context engineering, prompt caching, tool use, progressive disclosure, skills, MCP servers, agents, and agent harnesses - Practical experience with LLM provider APIs and gateways such as OpenAI Chat Completions and Responses APIs, the Anthropic Messages API, or OpenRouter - Ability to read, write, and structure Markdown-based systems, alongside TypeScript development experience and solid Git and GitHub fluency - Security-first mindset: considering what autonomous agents should not be allowed to do before shipping them - Strong written and spoken communication of deeply technical topics, with patience working with non-technical colleagues

Nice to have - Familiarity with AI software development techniques like spec-driven development or RPI (Research, Plan, Implement) - Experience with principal AI development harnesses such as Claude Code, Codex, or OpenCode - Familiarity with SDD tooling like Superpowers or Spec Kit - Experience running evaluations of LLM systems including evals, benchmarks, and regression testing of prompts - Ability to work autonomously and as an effective collaborator

Benefits and work setup - Fully remote work - Flexible paid time off - Eight weeks paid leave for primary caregivers, four weeks for secondary caregivers, plus a one-time baby bonus - Up to $500 in equipment support for a home office - Medical insurance - Learning and development opportunities - Monthly stipend for a preferred co-working space