Cloud & AI Deployment Engineer

Fairview Health Services•Minneapolis, MN

About The Position

The Cloud & AI Deployment Engineer designs, implements, and supports assigned Azure cloud infrastructure and deployment capabilities for AI and machine-learning solutions in partnership with central Cloud Engineering and infrastructure owners. The role combines hands-on cloud engineering with collaboration across DevOps, Security, and AI/ML teams to deliver reliable, secure environments that can be supported and maintained long term. It follows enterprise infrastructure-as-code, pull-request, deployment, and cloud-governance practices, including FinOps tagging. The engineer identifies gaps and helps establish AI-specific cloud guardrails, reusable patterns, and implementation guidelines with responsible enterprise teams. The position supports cloud and infrastructure delivery; MLOps retains deployed-model operations, while data scientists and designated reviewers retain scientific method validation. As the AI & Automation team’s dedicated cloud engineering resource, the role enables AI engineers through secure, repeatable Azure environments and deployment patterns while working alongside existing enterprise and contracted cloud resources. It may also support AWS and hybrid-cloud needs where applicable.

Requirements

B.S./B.A. in Computer Science, Engineering, Information Technology, Information Systems, or a related field, or a combination of education and experience equivalent to a bachelor’s degree.
Three years of experience in cloud engineering, DevOps, platform engineering, infrastructure automation, or related work, including demonstrated hands-on experience with Microsoft Azure.
Demonstrated hands-on experience with Azure cloud engineering and deployment in an enterprise environment.
Experience implementing and maintaining infrastructure as code and reusable deployment configurations.
Experience with source control, pull requests, CI/CD, and controlled deployment processes using GitHub, Azure DevOps, or comparable enterprise-managed tools.
Ability to implement and troubleshoot cloud compute, storage, networking, and access configurations in partnership with infrastructure teams.
Experience applying cloud governance, resource tagging, and enterprise deployment and support standards.
Understanding of security best practices, role-based access control, encryption, and sensitive-data handling in cloud environments.
Experience with monitoring, logging, operational troubleshooting, and documented production support for cloud-hosted services.
Ability to identify gaps and develop clear technical guidelines, guardrails, and reusable implementation patterns with enterprise owners.
Ability to independently deliver assigned work, communicate technical tradeoffs, and collaborate with central cloud, infrastructure, DevOps, data/API, Security, and AI/ML teams.

Nice To Haves

Experience supporting AI/ML deployments or AI applications in Azure.
Experience in healthcare, health technology, or another regulated industry handling sensitive data.
Experience with containers and deployment patterns for cloud-hosted applications and services.
Experience with AWS or hybrid-cloud environments and applying common standards across cloud platforms.

Responsibilities

Design, implement, configure, and maintain assigned Azure infrastructure for AI/ML solutions in partnership with central Cloud Engineering and infrastructure teams.
Coordinate compute, storage, networking, access, and dependencies using approved cloud capabilities.
Build and maintain version-controlled infrastructure and deployment configurations.
Use pull requests, peer review, testing, and enterprise CI/CD processes for repeatable deployment, promotion, rollback, and recovery; align GitHub and DevOps practices with designated enterprise owners.
Identify implementation gaps and help develop and apply approved AI cloud guidelines, reusable patterns, and guardrails with central Cloud Engineering, Architecture, and Security.
Apply enterprise-required resource tagging and cloud-governance practices to support ownership, cost visibility, and efficient resource use; address governance gaps with responsible enterprise teams.
Implement approved encryption, access-control, and sensitive-data protections, coordinating security requirements with security and infrastructure owners and bringing tooling or design exceptions through established review processes.
Document configurations, dependencies, monitoring, logging, operational procedures, recovery steps, and escalation paths with responsible service owners before production use.
Coordinate ongoing support, including coverage required for critical services, rather than relying on an individual project owner.
Monitor and troubleshoot assigned cloud infrastructure and deployment processes; work with MLOps and enterprise service owners to investigate failures, resolve infrastructure dependencies, and maintain availability and performance.
Partner with central cloud, infrastructure, DevOps, data/API, and AI/ML teams to deliver supported environments and deployment capabilities.
Clarify implementation and support responsibilities and contribute to updating responsibility assignments as enterprise platform ownership evolves.
Maintain technical documentation, reusable deployment patterns, and support information.
Provide constructive technical feedback, share implementation guidance, and improve reproducibility, efficiency, long-term support, and maintainability across AI cloud deployments.