Advanced AI Architect

American Electric Power•Columbus, OH

About The Position

At American Electric Power (AEP), 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 focused on architecture leadership, enablement, and technical direction across internal teams and vendor partners. The role requires 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

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.
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.

Responsibilities

Define AI/ML and GenAI reference architectures, components, and patterns (apps, pipelines, integrations).
Translate business needs into scalable, secure designs that 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) that 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.