Basic Qualifications
- 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.
- Employees must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire.
- This position may require access to export-controlled technology; therefore, candidates must be a U.S. person as defined by U.S. export-control laws.
Preferred Qualifications
- 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 that integrate cloud, on-premises, enterprise, and operational technology environments.
- Experience operationalizing machine-learning or AI models for production use, 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.
Position Summary
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. This role designs, builds, supports, and continuously improves Azure-based data, AI, and integration capabilities that enable secure, reliable, and scalable business and manufacturing solutions.
The successful candidate will combine cloud engineering, data and AI platform experience, and strong operational discipline. They will work with infrastructure, security, application, data, and manufacturing teams to develop and operationalize AI and machine-learning solutions, including their integration with process-control environments. The role requires sound judgement, excellent troubleshooting skills, and the ability to translate business and operational requirements into supportable technical solutions.
Responsibilities
- Design, implement, administer, and support Azure cloud services used for AI, data, and business applications, including compute, storage, networking, identity, security, and monitoring capabilities.
- 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 capabilities to enable governed development, evaluation, deployment, and monitoring of generative-AI and other AI solutions.
- Assist data scientists, application teams, and business stakeholders in operationalizing models for reliable integration with process-control environments, ensuring solutions are appropriately 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 established 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 accurate 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.
Position Summary
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. This role designs, builds, supports, and continuously improves Azure-based data, AI, and integration capabilities that enable secure, reliable, and scalable business and manufacturing solutions.
The successful candidate will combine cloud engineering, data and AI platform experience, and strong operational discipline. They will work with infrastructure, security, application, data, and manufacturing teams to develop and operationalize AI and machine-learning solutions, including their integration with process-control environments. The role requires sound judgement, excellent troubleshooting skills, and the ability to translate business and operational requirements into supportable technical solutions.
Responsibilities
- Design, implement, administer, and support Azure cloud services used for AI, data, and business applications, including compute, storage, networking, identity, security, and monitoring capabilities.
- 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 capabilities to enable governed development, evaluation, deployment, and monitoring of generative-AI and other AI solutions.
- Assist data scientists, application teams, and business stakeholders in operationalizing models for reliable integration with process-control environments, ensuring solutions are appropriately 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 established 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 accurate 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.