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$160/hr–$200/hr
Full-time
Posted 11d ago
Apply by Oct 27
~40 hrs/week
Remote in United States
Responsibilities
You will build reference applications, quickstarts, and sample repositories to enable developers to adopt ArangoDB's AI tools effectively. Additionally, you will serve as a technical advocate by publishing benchmarks, writing content, and providing direct feedback to the product team.
Requirements
You must be an experienced engineer with hands-on production experience in LLM applications, RAG pipelines, and retrieval strategies. Strong proficiency in Python and at least one other language like TypeScript, Go, or Java is required, along with excellent communication skills for technical advocacy.
Full job description
Developer Relations
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 atarango.ai,LinkedIn, andG2.
Location
Remote - United States preferably Bay Area location
About the Role
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.
Key Responsibilities
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.
Skills and Experience Required
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
What Success Looks Like
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.
What Makes Arango Special?
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.
Arango provides the trusted data foundation for enterprise AI through its Contextual Data Platform, transforming fragmented enterprise data into a contextual data layer that enables AI systems to operate with business context at scale.
The Arango Contextual Data Platform gives developers a single, integrated environment to build and run AI-powered applications, agents, and assistants without stitching together multiple databases, search systems, and AI infrastructure. It combines a multimodel data foundation—graph, vector, document, and key-value—with built-in search and governance capabilities. This enables AI agents to ground responses in enterprise context, navigate relationships across data types, and take reliable, state-aware actions based on real-time data.
The Arango Agentic AI Suite is the agent layer of the Arango Contextual Data Platform—connecting LLMs to governed enterprise data through contextual retrieval and tool-based execution.
It turns fragmented data into context-rich knowledge graphs using AutoGraph and multi-modal RAG pipelines (GraphRAG, VectorRAG, and HybridRAG), enabling agents to reason over relationships—not just retrieve documents.
Core capabilities include:
→ multimodal ingestion + AutoGraph for automated knowledge graph creation
→ GraphRAG and natural-language querying via AQLizer
→ Graph Visualizer for interactive exploration and explainability
→ Graph Analytics and GraphML for relationship-driven insights
→ integration with LLMs and ML tooling for agent execution
The result: context-aware, governed agent workflows that enable agents to reason, decide, and act with full business context, delivering explainable and auditable outcomes.
Trusted by NVIDIA, HPE, the London Stock Exchange, the U.S. Air Force, NIH, and Articul8, Arango powers enterprise AI with context, confidence, and scale.
We are a proud member of the NVIDIA Inception Program and the AWS ISV Accelerate Program. Learn more at arango.ai, LinkedIn, and YouTube.
Offices: 548 Market St, San Francisco, CA 94104, US
AI Data PlatformVector StoreGenAIGraphRAGHybridRAGGraphMLNetworkXNvidiaAgentic AIArtificial Intelligence
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