AI Knowledge Base Coordinator

API, LLC•West Lake Hills, TX

About The Position

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.

Requirements

Project Management Experience: Demonstrated track record of coordinating complex, multi-stakeholder initiatives end-to-end—prioritizing work, managing dependencies, and keeping teams accountable to milestones.
The "Systems Thinker": You have experience in Knowledge Management, Content Strategy, or Operations. You love creating order out of chaos and building processes that stay fixed.
Non-Technical but Tech-Savvy: You don’t need to write code, but you understand how data flows. You are comfortable using internal dashboards and tools to diagnose issues and track performance.
Master Communicator: You can translate technical "data gaps" into clear, actionable requests for clients and internal stakeholders.
Comfort with Ambiguity: You are excited by a role that will grow and change. You aren't looking for a "maintenance" job; you want to be the person who builds the system others will eventually maintain.

Nice To Haves

Coursework or certifications related to data management, information architecture, content strategy, or AI/ML fundamentals are a plus.
Ongoing professional development in AI, knowledge systems, or process design is strongly encouraged.

Responsibilities

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.
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.
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.