About the role
About Lago
Lago is the leading open-source billing platform for usage-based pricing. We power billing for companies like PayPal, CoreWeave, Mistral AI, and Synthesia — handling real-time metering, hybrid pricing models, credit wallets, and multi-entity invoicing. We raised $22M (Series A led by FirstMark, seed backed by Y Combinator) and are growing fast across AI/ML, developer infrastructure, and enterprise segments.
The role
We’ve been building AI-powered internal agents that automate key GTM workflows — lead qualification, prospect research, outreach drafting, customer monitoring, and more. These agents run on Claude Code, connect to our CRM, Slack, and email, and have been iteratively refined with real production data over several months. They work. But right now they’re monolithic skills, operated by one person. We want to turn them into a production-grade multi-agent platform that the whole team can use — and that improves itself over time. You’ll work directly with Lago’s founders to own this transformation: from working prototypes to a scalable internal AI platform that becomes the backbone of our GTM operations.
What you’ll do
Decompose monolithic AI skills into composable agents. Our current automations are single mega-prompts that handle enrichment, scoring, cross-referencing, drafting, and monitoring in one pass. You’ll break them into discrete, testable agents with clear inputs, outputs, and failure modes — built on Claude Code and the Claude Agent SDK.
Make the system multi-user. Today, one person triggers everything. You’ll design it so multiple team members can interact with the agents from their own context — different permissions, different views, different triggers — with a shared state layer that keeps everyone in sync.
Build self-improvement loops. These agents already incorporate learned corrections from real-world feedback. But today those corrections are manually written into prompts. You’ll build systems where feedback signals are automatically captured, calibration rules are proposed and reviewed, and the agents track their own accuracy over time.
Lead qualification is the first use case, not the last. We have plans for deal-stage automation, customer intelligence, renewal risk scoring, competitive monitoring, and more. You’ll establish the patterns, tooling, and architecture that make spinning up new agents fast and reliable.
This isn’t research. Every agent you build runs in production, handles edge cases gracefully, and earns the trust of the people using it daily. You’ll instrument everything and use the data to improve.
