Cloud & AI Deployment Engineer

This role will serve as the AI & Automation team’s dedicated cloud engineering resource, enabling AI engineers through secure, repeatable Azure environments and deployment patterns while working alongside existing enterprise and contracted cloud resources. Responsibilities/Job Description: 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.

Responsibilities/Job Description:

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. The engineer follows enterprise infrastructure-as-code, pull-request, deployment, and cloud-governance practices, including FinOps tagging.

 

The role identifies gaps and helps establish AI-specific cloud guardrails, reusable patterns, and implementation guidelines with the responsible enterprise teams. The position supports cloud and infrastructure delivery; MLOps retains deployed-model operations, and data scientists and designated reviewers retain scientific method validation.

 

This role will serve as the AI & Automation team’s dedicated cloud engineering resource, enabling AI engineers through secure, repeatable Azure environments and deployment patterns while working alongside existing enterprise and contracted cloud resources. The role may also support AWS and hybrid-cloud needs where applicable.

 

Responsibilities

 

Azure cloud implementation. Design, implement, configure, and maintain assigned Azure infrastructure supporting AI/ML solutions in partnership with central Cloud Engineering and infrastructure teams. Coordinate compute, storage, networking, access, and dependencies using approved cloud capabilities.

 

Infrastructure as code and deployment. 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.

 

AI cloud guardrails and standards. Identify implementation gaps and help develop and apply approved AI cloud guidelines, reusable patterns, and guardrails with central Cloud Engineering, Architecture, and Security. Ensure deployments follow shared standards rather than isolated, project-specific practices.

 

Cloud governance and FinOps. Apply enterprise-required resource tagging and cloud-governance practices to support ownership, cost visibility, and efficient resource use. Work with the responsible enterprise teams to address tagging and governance gaps and maintain standards across cloud implementations.

 

Security and access. Implement approved encryption, access-control, and sensitive-data protections for cloud resources. Coordinate security requirements with security and infrastructure owners and bring needed tooling or design exceptions through established review processes.

 

Production readiness and support. Document configurations, dependencies, monitoring, logging, operational procedures, recovery steps, and escalation paths with responsible service owners before production use. Coordinate ongoing support, including the coverage required for critical services, rather than relying on an individual project owner.

 

Reliability and troubleshooting. 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.

 

Cross-team delivery and ownership. 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.

 

Documentation and continuous improvement. Maintain technical documentation, reusable deployment patterns, and support information. Provide constructive technical feedback, share implementation guidance, and improve reproducibility, efficiency, and long-term support and maintainability across AI cloud deployments.

 

Required Qualifications

• 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.

Preferred Qualifications

• 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.

Qualifications:

$109,449.60- $154,523.20 Annual