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# Cloud & AI Platform Engineer

- **Company:** INDUS
- **Location:** Atlanta, GA, United States
- **Contract:** Temporary to permanent
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Data Centers, DevOps, Microprocessors, Github, Oracle (Applications), Ansible, Software Deployment, Software Engineering, Oracle Fusion Middleware, AI Infrastructure, Microsoft Power Automate, Delivery Pipeline, Multi-Cloud, Netsuite Erp, Data Strategy, Containerization, AI Platforms, Kubernetes, Terraform, Software Version Control, Servicenow, Vmware
- **Published:** September 25, 2026
- **Apply:** https://www.dice.com/job-detail/cffe173a-5eee-4187-993e-2e857e903657

## About the Role

* Strong hands-on experience with Kubernetes in multi-cloud environments
 * Experience with Azure and AWS (Oracle Fusion Cloud ERP exposure is a plus but not required this can be learned on the job)
 * Experience with enterprise landing zones
 * Proficiency with Terraform and Ansible (or comparable IaC/automation tools)
 * Experience with DevOps/CI-CD pipeline design and operations, ideally with GitHub-based workflows
 * Understanding of foundational AI infrastructure concepts cloud AI services, containerized AI workloads, and deployment patterns (not application-level AI/ML development)
 * Comfort operating in ambiguous, evolving environments where governance and standards are still being established
 * Strong communication skills this role will need to train and enable other team members over time
 * Background in platform engineering / cloud infrastructure engineering / DevOps engineering more so than traditional application development

Nice to Have:

 * Familiarity with Microsoft Copilot and Microsoft-centric development ecosystems
 * Experience with ServiceNow-integrated provisioning workflows
 * Experience operationalizing on-premises AI infrastructure to reduce cloud/token costs

## Description

* Build and operate secure, reusable infrastructure across Azure, AWS, on-premises data centers, and Oracle environments to support application teams' access to internal systems, GitHub-based source control, and standardized deployment pipelines
 * Support and accelerate the migration of internal applications from VMware to Kubernetes-based containerization, enabling workload portability across cloud and on-prem environments
 * Help mature the organization's Infrastructure-as-a-Service model enabling developers to request compute resources (e.g., CPUs) via ServiceNow with automated provisioning on the back end (a longer-term, 1 2 year capability build)
 * Extend and enhance existing Terraform and Ansible automation to support additional developers and teams adopting the platform and Kubernetes deployment model
 * Support GitHub-based deployment workflows, helping teams commit and deploy code through automated CI/CD pipelines
 * Provide foundational support for AI infrastructure including cloud and potentially on-premises AI workloads understanding containerization, deployment, and infrastructure needs for AI systems (application-level AI development is not required)
 * Help establish practical infrastructure and platform standards while the organization's broader AI governance and data strategy are still being defined
 * Act as a platform owner who can reduce recurring operational dependency on the existing infrastructure leadership team, and help train internal staff on platform and AI infrastructure best practices