The opportunity
AI frameworks like LangChain, LlamaIndex, and n8n are quickly becoming the default way developers build with AI — and MongoDB wants to be the data platform that shows up everywhere they build. As part of the AI Builders Experience (ABX) org, we're standing up a brand new…
What you'll do
Design, build, test, and ship MongoDB integrations across a broad AI: framework ecosystem that includes technologies like LangChain, LangGraph, LlamaIndex, n8n, and Mastra, owning them from first exploratory spike through production release and ongoing maintenance.
Take ownership of ambiguous problems and drive them to completion: independently, making sound decisions as requirements, frameworks, and customer needs evolve.
Do the initial, exploratory work: assess a new framework, define the smallest useful version of an integration, get it in front of users quickly, and make a clear recommendation about whether and how to invest further.
Contribute high-quality code upstream to repositories MongoDB doesn't own,: maintaining a high standard of professionalism and courtesy while working through external maintainer review cycles.
Build and maintain the team's engineering practices for a broad integration: portfolio, including CI/CD, automated testing across framework and server versions, version compatibility awareness, and release readiness.
Own the operational health of the integrations you build: monitoring, upgrades, breaking-change response, issue triage, and follow-through after launch.
What they're looking for
- Strong background in building core components for scalable, high-availability: services, developer platforms, or libraries
- + years of experience building backend systems or developer-facing libraries,: with strong proficiency in Python and/or TypeScript
- Proven success in designing, writing, testing, debugging, and performance: tuning software in large, long-lived code bases, including code that other developers depend on
- Track record of identifying problems, implementing solutions, and delivering: complex projects independently with minimal guidance
- A product-minded approach to engineering, with the judgment to make pragmatic: tradeoffs between fast time-to-market and long-term maintainability, and the comfort to operate amid ambiguity and rapidly evolving technologies
- Demonstrated ability to context switch across a portfolio of projects while: maintaining sound prioritization and quality.
- Strong and demonstrated interest in modern AI builder workflows and the: developer tooling landscape: AI frameworks, agentic patterns, RAG and retrieval, embedding, vector search, or adjacent areas.
- Excellent verbal and written technical communication skills, including the: ability to write things down clearly for partners you won't always overlap with in real time.
