AI Ops Engineer (FBOS)

Job summary

Responsibilities

Own internal AI systems that make Lovable run faster than it should.

• Define how AI-native companies operate: Help build the playbook. What we learn here scales to every company using Lovable.
• Build internal tools and workflows: Using Lovable, AI agents, no-code, raw code—whatever gets the job done fastest. Lovable on Lovable first, always.
• Turn company data into operational leverage: Connect and enrich data across our stack, then put it directly to work, powering automations, internal tools, AI agents…
• Deploy AI agent armies: Claude/ChatGPT with MCPs configured so every teammate has AI working for them, not the other way around.
• Squeeze value from our stack: Make the AI features in Slack, Linear, Notion, Ashby, etc. actually deliver.

What they're looking for

• Shipped production automations at a company: You've built workflow systems at scale that ran without you. Actual business-critical automations used by teams.
• Engineering brain, modern toolkit: You understand how systems actually work (APIs, databases, architecture) and can review code confidently.
• Data is part of your building material: You can turn fragmented data across tools, databases, and external sources into reliable, usable systems.
• AI tool obsessed: You're always testing the latest models, apps, and workflows before anyone else. You've deployed internal AI tools (Claude/ChatGPT with custom contexts, AI…
• Can't unsee inefficiency: You see the whole company as one machine. Once you spot a broken process, you can't leave it alone.

Tech stack

• n8n
• SQL