Cloud & AI Engineer

Howmet Aerospace•Whitehall, MI

About The Position

Howmet Engines Shared Services is seeking a Cloud and AI Engineer to join a close, cross-functional team supporting enterprise IT infrastructure, cloud services, and the responsible adoption of artificial intelligence across the business. The role designs, builds, supports, and continuously improves Azure-based data, AI, and integration capabilities for secure, reliable, and scalable business and manufacturing solutions. The successful candidate combines cloud engineering, data and AI platform experience, and operational discipline, working with infrastructure, security, application, data, and manufacturing teams to develop and operationalize AI and machine-learning solutions, including integration with process-control environments. Sound judgment, strong troubleshooting skills, and the ability to translate business and operational requirements into supportable technical solutions are required.

Requirements

Bachelor’s degree in Computer Science, Information Technology, Engineering, Data Science, or a related discipline; or six years of relevant equivalent experience.
Three or more years of experience supporting cloud, data, AI, machine-learning, software, or hybrid technology environments.
Experience with Microsoft Azure services and cloud infrastructure concepts, including identity, networking, security, compute, storage, and monitoring.
Experience developing, deploying, supporting, or integrating data, analytics, machine-learning, or AI solutions.
Ability to work effectively with technical and non-technical stakeholders in a manufacturing or enterprise environment.
Willingness and ability to travel up to 25% as required.
Legally authorized to work in the United States; employment eligibility verification is required at hire.
Must be a U.S. person as defined by U.S. export-control laws because the position may require access to export-controlled technology.

Nice To Haves

Experience with Azure Machine Learning, including model training, registration, managed endpoints, pipelines, monitoring, and MLOps practices.
Experience with Azure AI Foundry, Azure OpenAI, Azure AI services, prompt engineering, model evaluation, and responsible-AI controls.
Experience designing Azure architectures integrating cloud, on-premises, enterprise, and operational technology environments.
Experience operationalizing machine-learning or AI models for production, including deployment automation, model monitoring, drift detection, versioning, and rollback approaches.
Familiarity with secure integration patterns and data flows involving process-control, manufacturing, industrial, or operational technology environments.
Experience with Azure DevOps, Git, CI/CD pipelines, PowerShell, Python, Terraform, ARM templates, or other automation and infrastructure-as-code tools.
Knowledge of Microsoft Entra ID, Azure networking, private endpoints, role-based access control, logging, monitoring, and security best practices.
Experience with data platforms, APIs, message-based integrations, SQL, data pipelines, or data-lake architectures.
Strong troubleshooting, analytical, organizational, written, and verbal communication skills.
Ability to work independently while contributing effectively in a collaborative, cross-functional team environment.

Responsibilities

Design, implement, administer, and support Azure cloud services for AI, data, and business applications, including compute, storage, networking, identity, security, and monitoring.
Build and support machine-learning development and deployment workflows using Azure Machine Learning, including data preparation, experiment tracking, model registration, deployment, monitoring, and lifecycle management.
Configure and support Azure AI Foundry for governed development, evaluation, deployment, and monitoring of generative-AI and other AI solutions.
Help data scientists, application teams, and business stakeholders operationalize models for reliable integration with process-control environments, ensuring solutions are tested, monitored, documented, and supportable.
Develop and maintain secure integration patterns between cloud-based AI services, enterprise applications, data platforms, and manufacturing or operational technology environments.
Apply security, identity, networking, configuration, logging, and data-protection controls in accordance with company standards and architecture.
Collaborate with cybersecurity, infrastructure, data, application, and manufacturing teams to identify and remediate vulnerabilities, incidents, configuration issues, and operational risks.
Monitor AI and cloud services for availability, performance, cost, model quality, drift, and operational health; investigate and resolve issues in a timely manner.
Contribute to capacity planning, performance optimization, high availability, backup, disaster recovery, and business-continuity requirements for cloud and AI services.
Automate repeatable cloud, AI, deployment, and operational activities using scripting and infrastructure-as-code practices.
Maintain technical documentation, including architectures, integrations, operational runbooks, support procedures, and solution configurations.
Participate in the full technology lifecycle, including requirements gathering, design, testing, implementation, support, upgrades, and continuous improvement.