Lead AI Platform Infrastructure Architect

Rockwell Automation•Mayfield Heights, OH

7d•Hybrid

About The Position

As our AI Platform Architect, you will develop platforms that make it simple for teams across Rockwell to use AI. You will deliver working AI tools using infrastructure as code and create platforms that accelerate how quickly we can adopt and benefit from AI. This is a hands-on role. You will write code, build pipelines, deploy infrastructure, and own what you ship. You will also partner with engineering, data, and security teams to make sure what you build fits into how we work. You will report to the Leader of Infrastructure Platform Automation as senior-level individual contributor, expected to influence across Rockwell.

Requirements

Bachelor's degree or equivalent years of relevant experience.
Legal authorization to work in the U.S. We will not sponsor individuals for employment visas, now or in the future, for this job opening.
Ability to travel domestically or internationally up to 10%.

Nice To Haves

Typically requires 12+ years of experience in infrastructure architecture, platform engineering, or cloud engineering roles.
Degree in Computer Science, Engineering, or a related technical field.
Background in cloud infrastructure (Azure, AWS, or GCP) and hybrid enterprise environments.
Hands-on experience designing platforms using containers, Kubernetes, and infrastructure-as-code.
Familiarity with AI/ML platform requirements, including GPU acceleration, high-performance storage, and network throughput.
Experience integrating security, identity, and compliance controls into shared platforms.
Experience designing enterprise-ready platforms that balance innovation, risk, and operational scalability.

Responsibilities

Build AI Platforms: Deliver platforms that support the full AI lifecycle, from data ingestion and model training to deployment and monitoring.
Create reusable services and templates so teams can go from idea to production with less effort.
Ensure platforms are reliable, scalable, and straightforward to use.
Deliver Infrastructure as Code: Develop all platform components using infrastructure as code.
Create repeatable deployment patterns and automated pipelines.
Lead the automation that provisions, updates, and scales platform environments.
Enable AI Adoption: Build practical tools and services that reduce the barriers to using AI across the enterprise.
Create standardized paths for teams to build, test, and deploy AI solutions.
Focus on making the platform easy to adopt so teams spend their time on outcomes, not on setup.
Integrate and Operate: Connect data pipelines, model services, APIs, and monitoring into a cohesive platform.
Support both real-time and batch AI workloads.
Ensure high availability, performance, cost efficiency, and security across all platform components.