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About Arango:

Arango delivers a unified, natively multimodel contextual data platform that powers AI agents, assistants, and applications with the unified, current, and trusted business context needed to reason, decide, and act at scale.

The Arango Contextual Data Platform connects fragmented enterprise data with LLMs, copilots, and AI agents through a simplified architecture delivered out of the box. By combining graph, vector, document, key-value, and search capabilities in a single platform, Arango eliminates the complex stacks many organizations build to operationalize enterprise AI.

Trusted by organizations including NVIDIA, HPE, the London Stock Exchange, PSI CRO, the U.S. Air Force, NIH, Siemens, Transient.AI, Matpriskollen, and Articul8, Arango helps enterprises move from AI pilots to reliable production systems faster while lowering infrastructure complexity and total cost of ownership. Arango is a proud member of the NVIDIA Inception Program and the AWS ISV Accelerate Program. Learn more at arango.ai, LinkedIn, and G2.

Remote - United States preferably Bay Area location

Step into Arango’s first dedicated developer advocacy role, reporting directly to the Chief Product Officer.

Our foundation powers two breakthroughs: AutoGraph instantly builds knowledge graphs from enterprise documents, no manual ontology design required. AutoRAG dynamically selects the optimal retrieval method (GraphRAG, VectorRAG, or hybrid) for every query. Both integrate seamlessly with LangChain, LlamaIndex, and MCP to amplify, not replace, existing tools.

Your mission: enable developers to go from install to a working GraphRAG app on their own data in under 30 minutes, and to publicly demonstrate that your solution outperforms DIY alternatives.

You’ll shape and build Arango’s developer community, content, and event presence from scratch—defining what developer relations means here. Reporting into product (not marketing) ensures that your feedback directly shapes our roadmap, and your time is spent in the codebase, docs, and issue tracker.

Build things developers want to copy. Create reference applications, quickstarts, and sample repositories that solve real, end-to-end problems—from multi-hop question answering over document corpora, to agents that traverse entity relationships where vector search falls short, to integrations developers can easily adopt in their projects.

Publish honest benchmarks. Acknowledge where GraphRAG excels—and where vanilla RAG outperforms it—reflecting the published literature. Run standard evaluation harnesses (like GraphRAG-Bench), publish wins, and be transparent about limitations.

Teach in public through technical posts, talks, workshops, and live builds. Prioritize depth over volume.

Engage where AI engineers already are. GraphRAG and knowledge-graph communities, AI engineering meetups, framework ecosystems, and relevant conferences—both online and in person.

Close the loop to product. Serve as the critical link between developers and our product team, delivering specific, prioritized feedback—not just general sentiment.

• You are an engineer. Title aside, what matters is your ability to build and debug real, non-trivial applications.
• You have hands-on, production experience with LLM applications and retrieval: RAG pipelines, embeddings, chunking strategies, and evaluation. You recognize that retrieval quality issues go deeper.
• Strong Python skills, plus proficiency in at least one of TypeScript/JavaScript, Go, or Java.
• You’ve written technical content that developers actually finished, and you can point us to examples.
• You can own a room: giving talks, running workshops, or live-coding, and handle tough questions with confidence.
• You’re comfortable with Docker and Kubernetes deployments.

Strongly preferred

• Experience with graph databases, knowledge graphs, or GraphRAG. If you’ve built a knowledge graph by hand, you know why AutoGraph matters.
• Familiarity with LangChain, LlamaIndex, MCP, or similar orchestration frameworks.
• You’ve run evaluation harnesses in real-world scenarios and have informed opinions about LLM-as-judge approaches.
• Prior experience as an early or founding developer relations hire at an infrastructure or developer tools company.
• Preferred Experience building agents at scale and for large enterprises

• Developers adopt and build on our reference applications and architectural blueprints.
• A higher percentage of trial users reach a working application.
• The roadmap shifts based on your insights from real developer feedback.

At Arango, we believe that AI is only as powerful as the data foundation. Our mission is to help organizations build AI systems that can reason, decide and act based on unified, current, and trusted business context at scale. We are helping define a new category of infrastructure: the contextual data layer for AI.

Working at Arango means:

• Contributing to cutting-edge AI and data infrastructure
• Collaborating with experienced engineers, marketers, and product leaders
• Helping shape how enterprises build AI-powered applications

If you're excited about the intersection of AI, data, and social media, we’d love to hear from you.

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