Act as the primary technical advisor to Onyx’s rapidly growing customer base, providing expert support and building strong relationships
Guide customers through their self-hosted deployments across different clouds (AWS, GCP, Azure, etc.) and deployment methods (Docker Compose, Kubernetes, AWS ECS, Helm, etc.)
Debug and patch tricky, deployment-specific issues with customers
Work with our open source community to resolve common setup issues.
Contribute features to eliminate common deployment issues
Create a seamless onboarding experience by developing comprehensive documentation that enables open-source users to easily deploy Onyx in their own cloud environments
Work with customers to find the best use cases for Onyx, maximizing our impact and value
Be the voice of the customer and bridge the gap between customers and product. Relay product insights and customer feedback to founders/engineering and help shape the direction of product

Have 2+ years of experience as a Customer Engineer / Solutions Engineer / Deployed Engineer at a enterprise facing software company OR have 2+ years of experience in DevOps / Software Engineering and have an interest in shifting to a more customer-facing role
Experience with Docker/Kubernetes, cloud technologies (AWS, GCP, Azure), and on-prem/cloud deployments
Are a great multitasker, able to stay organized among competing priorities, and can pivot and adapt at a moment’s notice
Excellent communication skills, with a strong ability to collaborate with others (both teammates and external customers). Ability to explain complex engineering/infrastructure concepts and tradeoffs to customers in a simple way.
Experience working in a fast-moving startup and in small teams with minimal guidance.

You’ve worked closely with customers to help them self-host software on their own infrastructure
Experience with technical writing / large scale documentation
Experience with Typescript/React/NextJS, Python, Relational DB (Postgres), and Vespa or Elasticsearch
Experience (or even just interest) in information retrieval and AI (NLP, Deep Learning, GenAI)

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