Job Title: Senior Cloud Platform Engineer (GenAI Platforms)
Location: Charlotte NC (5 Days onsite)
Duration: 12 months
Primary Skills
- GCP
- Azure
- Terraform
- Kubernetes
- OpenShift (OCP)
- Platform Engineering
- Observability
- SRE / SLOs
- Python
- GenAI Platforms
- Arize AI
- Claude Cowork
- HashiCorp Vault
- Internal Developer Portals
- LLMs
- RAG
- MLOps / LLMOps
Key Responsibilities
- Architect and implement enterprise-scale cloud platforms across GCP and Azure.
- Build secure landing zones cloud governance models and organizational policies.
- Design hybrid connectivity and cloud networking solutions.
- Develop Infrastructure as Code (IaC) using Terraform for scalable deployments.
- Manage Kubernetes platforms including GKE and OpenShift (OCP).
- Implement observability monitoring and SRE practices with defined SLOs/SLIs.
- Integrate Arize AI and GenAI observability solutions into AI platforms.
- Support GenAI LLM and RAG-based application deployments.
- Develop platform engineering solutions and internal developer portals.
- Implement secrets management and security controls using HashiCorp Vault.
- Collaborate with DevOps AI/ML and engineering teams for cloud automation initiatives.
- Support MLOps and LLMOps lifecycle management.
Required Qualifications
- 6 years of experience in cloud/platform engineering.
- Experience with Arize AI or AI observability platforms.
- Strong expertise in GCP and Azure cloud environments.
- Hands-on experience with Terraform Kubernetes and OpenShift.
- Experience implementing enterprise observability and SRE frameworks.
- Strong understanding of cloud governance networking and security.
- Proficiency in Python scripting and automation.
- Experience with GenAI platforms and modern AI infrastructure.
Preferred Qualifications
- Knowledge of LLM deployment architectures and RAG frameworks.
- Experience building internal developer platforms and self-service tooling.
- Exposure to enterprise AI governance and compliance standards.
Job Title: Senior Cloud Platform Engineer (GenAI Platforms) Location: Charlotte NC (5 Days onsite) Duration: 12 months Primary Skills GCP Azure Terraform Kubernetes OpenShift (OCP) Platform Engineering Observability SRE / SLOs Python GenAI Platforms Arize AI Claude Cowork HashiCorp Vault Interna...
Job Title: Senior Cloud Platform Engineer (GenAI Platforms)
Location: Charlotte NC (5 Days onsite)
Duration: 12 months
Primary Skills
- GCP
- Azure
- Terraform
- Kubernetes
- OpenShift (OCP)
- Platform Engineering
- Observability
- SRE / SLOs
- Python
- GenAI Platforms
- Arize AI
- Claude Cowork
- HashiCorp Vault
- Internal Developer Portals
- LLMs
- RAG
- MLOps / LLMOps
Key Responsibilities
- Architect and implement enterprise-scale cloud platforms across GCP and Azure.
- Build secure landing zones cloud governance models and organizational policies.
- Design hybrid connectivity and cloud networking solutions.
- Develop Infrastructure as Code (IaC) using Terraform for scalable deployments.
- Manage Kubernetes platforms including GKE and OpenShift (OCP).
- Implement observability monitoring and SRE practices with defined SLOs/SLIs.
- Integrate Arize AI and GenAI observability solutions into AI platforms.
- Support GenAI LLM and RAG-based application deployments.
- Develop platform engineering solutions and internal developer portals.
- Implement secrets management and security controls using HashiCorp Vault.
- Collaborate with DevOps AI/ML and engineering teams for cloud automation initiatives.
- Support MLOps and LLMOps lifecycle management.
Required Qualifications
- 6 years of experience in cloud/platform engineering.
- Experience with Arize AI or AI observability platforms.
- Strong expertise in GCP and Azure cloud environments.
- Hands-on experience with Terraform Kubernetes and OpenShift.
- Experience implementing enterprise observability and SRE frameworks.
- Strong understanding of cloud governance networking and security.
- Proficiency in Python scripting and automation.
- Experience with GenAI platforms and modern AI infrastructure.
Preferred Qualifications
- Knowledge of LLM deployment architectures and RAG frameworks.
- Experience building internal developer platforms and self-service tooling.
- Exposure to enterprise AI governance and compliance standards.
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