AI Knowledge Base Coordinator

You MUST reside in the Austin, Texas Metropolitan area to be considered for this position. Additionally, we do not sponsor any type of Visa.

POSITION SUMMARY

We are looking for an AI Knowledgebase Coordinator to manage information repositories that power our AI intelligence. This is a foundational role designed for a builder—someone who enjoys taking unstructured information and creating the rigorous processes required to make it "AI-ready."

You will act as the vital link between our business, our Data Science team, and our AI tools. Your mission is to ensure that as we scale, our AI systems are fed the highest quality "ground truth" data. In the new and exciting role, you won't just be managing data; you will be designing the blueprint for how we onboard information and maintain knowledge integrity at an enterprise scale.

RESPONSIBILITIES

1. Building the "Knowledge Playbook"

• Process Architecture: Design and implement the end-to-end workflow for how knowledge is captured, audited, and updated within our systems.
• Standardization: Establish the "Gold Standard" for what documentation must look like to be effective for AI, creating templates and requirements that will guide our knowledge onboarding experience.
• Scalability: Move our knowledge management from "manual/bespoke" to "systematic/repeatable," ensuring we can support a rapidly growing list of clients without a loss in AI performance.

2. Client Knowledge Onboarding

• Strategic Integration: Serve as the lead for client knowledge transfers. You will work with clients to navigate their internal repositories (Zendesk, Google Drive, Wikis) and ensure their data is correctly integrated into our AI ecosystem.
• Gap Remediation: Act as a consultant to our clients, helping them identify where their internal documentation is weak or outdated and guiding them on how to fix it at the source.

3. Data Science Partnership

• Internal Tool Mastery: Use proprietary diagnostic tools built by our Data Science team to identify "knowledge gaps"—areas where the AI is struggling because the source data is missing or unclear.
• Source-of-Truth Advocacy: Instead of "patching" the AI, you will lead the effort to resolve issues at the root. You will work across teams to update the original documentation, ensuring the AI is always drawing from the most accurate and current data available.
• Feedback Loops: Design the communication loop between the Data Science team’s technical insights and the business-level documentation updates.