AI Ops Engineer - Finance

• 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
• 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
• Level up the team: Turn everyone at Lovable into AI power users. Document what works. Scale it
• Own it end-to-end: You build it, you maintain it, you improve it

• 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. You move fast by choosing the right tool for the job: AI agents (Cursor, Claude Code etc) when they're faster, no-code (n8n, Lovable itself, Clay, etc.) when it fits, raw code when it's needed. Python, TypeScript, SQL etc
• 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 agents, MCPs, etc.) at a company and you're constantly experimenting with new ones
• Can't unsee inefficiency: You see the whole company as one machine. Once you spot a broken process, you can't leave it alone
• High agency: You don't wait for permission. You find the bottleneck, build the fix, and ship it

Ex-founder, worked at AI-native startup, or built automations across multiple business functions