Advanced AI Architect

American Electric Power•Columbus, OH

About The Position

At AEP, we’re more than just an energy company! We’re a team of dedicated professionals committed to delivering safe, reliable, and innovative energy solutions. Guided by our mission to put the customer first, we strive to exceed expectations by listening, responding, and continuously improving the way we serve our communities. If you're passionate about making a meaningful impact and being part of a forward-thinking organization, this is the company for you! Help Us Power the Future by Building a Scalable, Responsible Enterprise AI Foundation At American Electric Power, the AI Solutions & Enablement team is building the platforms, guardrails, and solution capabilities that empower teams across the company to use AI responsibly and at scale. We’re seeking an Advanced AI Architect to serve as the technical backbone for our AI ecosystem, shaping the enterprise AI foundation, defining scalable solution patterns, and translating responsible AI standards into practical engineering guardrails. This is a senior individual contributor role (not a managerial position). The focus is architecture leadership, enablement, and technical direction across internal teams and vendor partners, while expecting the depth and hands-on capability to selectively dive into code, pipelines, and production systems when needed to validate patterns, unblock delivery, or harden solutions.

Requirements

Design enterprise AI/ML and GenAI architectures and reusable patterns
Strong AI/ML workflows and practical MLOps knowledge; hands-on when needed
Experience enabling cloud AI platforms (Azure AI Foundry/ML/OpenAI and/or AWS Bedrock)
CI/CD and IaC expertise; familiarity with GitHub Actions, Terraform, MLflow, Helm, Argo (or equivalent)
Embed production readiness into patterns and standards (observability, reliability, scalability/availability, secure operations)
Security and governance fundamentals for AI (IAM/RBAC, secrets management, auditability, policy enforcement)
Vendor technical leadership: review delivered work, spot risks early, drive quality outcomes
Strong communication and influence without authority; balances standards with delivery realities
Bachelor's degree in computer science, engineering, or related technical field is required.
10 years of relevant work experience is required.
An equivalent combination of education and related experience may be considered.

Responsibilities

Define AI/ML and GenAI reference architectures, components, and patterns (apps, pipelines, integrations)
Translate business needs into scalable, secure designs teams and vendors can implement
Set lightweight, repeatable practices that balance rigor and speed (DevOps/MLOps)
Build prototypes and reference implementations to validate decisions, reduce risk, and accelerate adoption
Evolve internal AI platforms and dev environments (Azure AI Foundry, Azure ML, Azure OpenAI; as applicable, AWS Bedrock)
Standardize reusable GenAI patterns (e.g., embeddings, vector search) teams can apply safely
Create templates, starter kits, and repos 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)
Troubleshoot and unblock issues as needed (configuration, pipelines, infrastructure-as-code, operations)
Improve the AI path-to-production (packaging/versioning standards; deployment and monitoring expectations)
Define lifecycle patterns for training, evaluation, deployment, rollback, and monitoring (right-sized to use case and risk)
Establish pragmatic CI/CD and IaC patterns (e.g., GitHub Actions, Terraform, MLflow, Helm, Argo)
Ensure production readiness and operational quality (reliability, maintainability, secure operations)
Embed observability, reliability, and (as relevant) cost controls into reference designs and standards
Support deep debugging and remediation as needed to harden systems and validate patterns (not primary delivery ownership)
Design standard patterns for scalability, availability, and compliance requirements
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)
Run the AI delivery operating model: intake, prioritization, risk review, AI registration/review, playbooks
Review vendor architecture and code as needed to find gaps, harden solutions, and unblock progress
Provide hands-on guidance through standards, reviews, and targeted deep dives
Set technical direction, unblock teams, and align stakeholders through influence