Cloud & AI Infrastructure Engineer

Hanwha Energy USA•Houston, TX

17h

About The Position

Hanwha Energy USA is seeking a Cloud & AI Infrastructure Engineer to design, build, operate, and optimize the Azure and AI platform that supports internal applications, AI agents, copilots, automations, and enterprise data workloads. This role is responsible for providing a secure, reliable, and cost-effective cloud foundation as the AI and application landscape scales. The ideal candidate will bring strong hands-on experience with Azure infrastructure, infrastructure-as-code, CI/CD, monitoring, observability, and cloud cost optimization. This person will work closely with Application Development and Data Management to ensure AI-enabled solutions are production-ready, well-governed, and operationally supportable. This is not a general infrastructure support role. We are looking for an engineer who can help establish modern cloud engineering discipline around provisioning, deployment, logging, monitoring, security, and cost control for AI-enabled systems.

Requirements

Bachelor’s degree in Information Systems, Computer Science, Engineering, or a related field. Equivalent experience may be considered.
4+ years of experience in cloud engineering, platform engineering, DevOps, or infrastructure engineering.
Strong hands-on experience with Microsoft Azure services and cloud architecture.
Experience with infrastructure-as-code such as Terraform, Bicep, ARM templates, or similar tools.
Experience building and maintaining CI/CD pipelines for application and platform deployment.
Experience implementing monitoring, logging, alerting, and observability for production systems.
Understanding of identity, access control, security, and networking concepts in cloud environments.
Experience supporting production applications and cloud platforms with a focus on reliability and operational excellence.
Strong troubleshooting and problem-solving skills.
Ability to work effectively with software developers, data engineers, and technical leadership.

Nice To Haves

Experience supporting AI or machine learning workloads in Azure.
Familiarity with Azure OpenAI and the infrastructure patterns needed to support AI-enabled applications.
Experience with containerization, orchestration, or modern hosting models.
Experience with FinOps, cloud cost optimization, tagging governance, or cost reporting.
Experience with secrets management, policy enforcement, and secure deployment patterns.
Experience in environments with custom internal applications, integrations, and growing cloud platforms.
Familiarity with observability and telemetry practices for distributed systems and AI applications.

Responsibilities

Design and provision Azure and AI resources using infrastructure-as-code, ensuring consistency, scalability, and maintainability.
Establish and enforce standards for networking, identity, monitoring, security, tagging, and cloud resource governance.
Build and maintain CI/CD pipelines for AI-enabled applications, internal platforms, agents, and automations.
Implement and support logging, telemetry, monitoring, and observability practices for AI and cloud-based workloads.
Partner with Application Development and Data Management to ensure solutions are deployed and operated in a secure, reliable, and supportable manner.
Manage Azure and AI platform environments across development, testing, and production.
Monitor and optimize Azure and Azure OpenAI usage and spend, right-size resources, and help improve cloud cost visibility and efficiency.
Partner with Finance and IT leadership on cloud cost reporting, chargeback visibility, and optimization opportunities.
Support identity, access, secrets, and security practices required for enterprise cloud and AI workloads.
Help define and maintain operational standards for backup, resiliency, recovery, alerting, and support readiness.
Troubleshoot deployment, performance, connectivity, and infrastructure issues affecting applications, agents, and cloud services.
Contribute to architecture discussions related to hosting, integration, scaling, environment design, and operational controls.
Document cloud patterns, standards, deployment approaches, and support procedures in a clear and practical way.
Evaluate and introduce appropriate tools and patterns that improve reliability, delivery speed, visibility, and cost management.