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# Exposure Management Architect

- **Company:** OpenKyber LLC
- **Location:** Atlanta, GA, United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Adobe InDesign, Artificial Intelligence, Microsoft Azure, Code Generation, Information Systems, Data as a Services, Machine Learning, Information Technology, Machine Learning Operations
- **Published:** October 10, 2026
- **Apply:** https://www.careerjet.com/jobad/us993f854a57411f4779d1d391ce6733e3

## About the Role

* 15+ years of experience in IT
 * 5+ years of experience: In an Enterprise Architect role AI and ML Architectures
 * 2+ years of experience: Data-focused A1 tools Lead Enterprise Architecture initiatives
 * Proficient in tools such as Purview, Unity catalog, Erwin, or other semantic/metadata platforms
 * Deep experience with Microsoft Azure and their AI and data services
 * Experience working with Financial Services Industry
 * Bachelor's degree in computer science, Information Systems, Engineering, or equivalent experience through work experience

## Description

The Enterprise Architect will bring strong cross-domain expertise, strategic thinking, and executive presence. This is not just a governance-only role. You will BE a strategic leader, facilitator, and a diplomat-someone who can influence CXO-level stakeholders and remain deeply involved in execution. You will work across domains to lead enterprise-wide architecture decisions that prepare us for scalable data and AI modernization. In an individual contributor role, you will work in a fast-moving, mid-size, highly collaborative environment. You will report to the Vice President, Enterprise Architecture and Standards. The Day-to-Day:

 * Drive enterprise-level architecture across multiple business domains, ensuring alignment with organizational strategy
 * Lead discussions with senior stakeholders and bring clarity to complex technical decisions
 * Lead the standardization of metadata practices across domains, ensuring discoverability, lineage, and governance
 * Design and evolve enterprise-level semantic data models, including logical and conceptual models, ontologies, and domain definitions
 * Partner with product, engineering, data, and AI teams to ensure data supports reporting, analytics, and AI use cases
 * Provide feedback that directly shapes the next generation of AI models
 * Knowledge of Machine Learning Operations (MLOps) workflows and tools for deploying, managing, and monitoring AI models in production
 * Actively participate in design, discussions, and delivery-not just governance-with hands-on engagement
 * Review AI-generated code to ensure it is accurate, efficient, and high quality.
 * Stay current on AI and data trends to help the organization evolve.