AI DevOps Engineer for Enterprise AI Platform

Atkore International; Dallas (TX); On-site; USD 108,000 - 148,000; Full time

Benefits offered by this job

Medical, vision, and dental insurance

Life insurance

Short-term and long-term disability

401k

Paid Time Off

Paid holidays

Any leave required under federal,state

Job summary

Atkore is seeking an AI DevOps Engineer to design, build, and operate the foundational infrastructure for enterprise AI capabilities across the organization, including data platforms, GPUs, and secure APIs. The role reports to IT Enterprise Architecture and will be based in Dallas, TX.

You will partner with security, data services, and application teams to ensure compliant, scalable AI platforms, implement CI/CD and MLOps, and drive governance and risk controls.

Qualifications

• BS / BA in Computer Science, Business or related field.
• At least 5 years of experience in DevOps, Cloud Engineering, or Infrastructure Engineering roles.
• Expertise in containerization and orchestration technologies (e.g. Kubernetes, Docker) and infrastructureascode tools (e.g. Terraform).
• Hands-on experience with public cloud platforms (Azure preferred), including networking, compute, and identity services.
• Experience supporting AI/ML infrastructure platforms (e.g. Azure ML Studio, Mflow, etc)
• Strong understanding of enterprise security principles, identity management, and secure API design.
• Familiarity with data platforms and analytics ecosystems supporting structured and unstructured enterprise data.
• Familiarity with Software Development Life Cycle and Agile delivery processes.
• Experience integrating AI platforms with ERP, MES, or manufacturing systems within an industrial or manufacturing environment desired.
• Exposure to AI governance, model risk management, or responsible AI frameworks desired.
• Strong analytical, problem-solving, and collaboration skills.
• Ability to communicate effectively with both technical and non-technical stakeholders.
• Must embrace and foster an environment that supports our core values of Accountability, Teamwork, Integrity, Respect, Excellence

Responsibilities

• Design, deploy, and maintain cloud and hybrid infrastructure supporting AI workloads, including container orchestration, GPU-enabled compute, and data platform integration
• Engineer scalable, resilient environments to support AI agent execution, model serving, and experimentation across the enterprise
• Design and manage APIs and microservices that provide secure, controlled data access for AI agents and AI-enabled applications
• Integrate AI infrastructure with enterprise platforms such as data lakes, analytics environments, and business systems
• Implement CI/CD pipelines, infrastructure-as-code, and automation to support AI platform reliability and repeatability
• Establish MLOps practices for model deployment, monitoring, and lifecycle management in partnership with data and AI teams
• Ensure AI infrastructure complies with Atkore security, privacy, and data governance standards
• Collaborate with cybersecurity and risk teams to enforce secure identity, access management, and auditability across AI platforms
• Partner with Enterprise Architecture to align AI platform design to longterm technology standards and roadmaps
• Work closely with Data Engineering, Application Integration, and other IT teams to enable AI use cases across Atkore operations

Skills

DevOps

Cloud Engineering

Kubernetes

Docker

Terraform

Azure

AI/ML infrastructure

Security principles

Data platforms

Agile delivery

Education

BS / BA in Computer Science, Business or related field of study

Tools

Azure ML Studio

Terraform

Kubernetes

Docker

Job description

Atkore is seeking an AI DevOps Engineer to design, build, and operate the foundational infrastructure for enterprise AI capabilities across the organization, including data platforms, GPUs, and secure APIs. The role reports to IT Enterprise Architecture and will be based in Dallas, TX.

You will partner with security, data services, and application teams to ensure compliant, scalable AI platforms, implement CI/CD and MLOps, and drive governance and risk controls.