Enterprise Architect - AI

Job Posting End Date 08-17-2026 Please note the job posting will close on the day before the posting end date.

The power of AEP comes from our valued team of unique employees—the best in the business. We’re proud to be one of the largest electric energy companies in the U.S., powering over 5 million homes and businesses across 11 states. Guided by our mission to put the customer first, we’re passionate about making an innovative difference in the communities we serve—listening, responding, and continuously improving the way we deliver safe, reliable, and forward-thinking energy solutions. We’re powering the future—today. The Enterprise Architect of Artificial Intelligence is a senior individual contributor responsible for defining, governing, and advancing the enterprise AI architecture that enables scalable, secure, and responsible adoption of artificial intelligence across AEP. This role serves as the enterprise authority on AI architecture, shaping how AI is industrialized to drive business outcomes across grid operations, customer platforms, and corporate functions. Operating at the intersection of enterprise architecture, data, and advanced analytics, this position translates strategic business priorities into AI-enabled architecture, standards, and reference models. The Enterprise Architect of AI partners closely with engineering, data science, cybersecurity, and business technology leaders to ensure AI solutions are interoperable, compliant, resilient, and aligned with AEP’s enterprise technology strategy and regulatory environment.

What you’ll do: Essential Job Functions & Tasks Enterprise AI Architecture, Patterns & Technical Direction Define and maintain enterprise AI/ML and GenAI reference architectures, components, patterns, and standards across cloud, on-premises, and edge environments, and actively engage in the Enterprise Architecture Governance process. Translate business use cases (e.g., forecasting, optimization, automation, customer engagement) into scalable, secure, production-ready designs that internal teams and vendors can implement. Establish architectural blueprints for real-time, batch, and edge-based AI decisioning. Ensure alignment between AI solutions, enterprise data platforms, integration patterns, and core systems. Build prototypes and reference implementations to validate decisions, reduce risk, and accelerate adoption. AI Platforms, Tooling & GenAI Enablement Evolve internal AI platforms and developer environments (e.g., Azure AI Foundry, Azure ML, Azure OpenAI; as applicable, AWS Bedrock). Standardize reusable GenAI patterns (e.g., embeddings, vector search, retrieval-augmented generation) that teams can apply safely. Create templates, starter kits, and repositories to streamline build-to-deploy and reduce friction. Guide workflow and platform integrations for usability, reliability, and governance (e.g., OpenShift workflow engines; model registry/tooling). Evaluate emerging AI technologies and platforms for fit within AEP’s enterprise architecture and regulated environment. Support modernization of legacy systems to enable AI-enabled workflows and decision automation. Governance, Risk & Responsible AI Establish architectural guardrails for responsible AI, including model transparency, data privacy, security, and compliance. Translate responsible AI standards into guardrails, checklists, and controls teams can apply consistently. Automate responsible AI controls where practical (e.g., review gates, policy checks) aligned to higher-risk governance and approvals. Embed security and governance fundamentals into patterns (IAM/RBAC, auditability, policy enforcement). Ensure AI solutions align with enterprise governance, cybersecurity standards, and regulatory requirements. Provide architectural review and guidance for AI initiatives to mitigate technical risk and ensure long-term sustainability. Cross-Functional Collaboration & Vendor Leadership Act