Cloud Platform Engineer

Position overview

Technology environment

• GCP/Google Cloud Platform, Microsoft Azure, Terraform, Kubernetes/GKE, and OpenShift/OCP.
• Cloud networking, hybrid connectivity, landing zones, organization policies, and cloud governance.
• HashiCorp Vault, IAM, policy-as-code, secrets management, observability, SRE, and Python.
• Internal developer portals, MLOps/LLMOps, GenAI platforms, LLMs, RAG, Arize AI, and Claude Cowork.

Key responsibilities

• Design, build, and operate secure, scalable GCP and OpenShift/GKE platforms for GenAI, LLM, and RAG workloads.
• Provision infrastructure with Terraform, including landing zones, networking, organization policies, and hybrid GCP/Azure connectivity.
• Develop MLOps/LLMOps pipelines for model deployment, monitoring, and lifecycle management.
• Integrate platform and observability solutions such as Arize AI for GenAI and ML workloads.
• Build Kubernetes-based platform abstractions, internal developer portals, and self-service environments.
• Establish security, governance, IAM, secrets management, and policy-as-code using Vault and related technologies.
• Develop observability, reliability, and SRE/SLO practices for cloud and GenAI platform services.
• Operate Kubernetes and OpenShift environments, including GKE and OCP, and maintain hybrid-cloud networking.
• Collaborate with data science, ML engineering, DevOps, and application teams to onboard LLMs, APIs, inference services, and GenAI workloads.
• Automate operations with Python, Terraform, and cloud-native tools; improve developer experience through self-service and standard deployment patterns.

Required skills

• Hands-on experience with GCP and/or Azure; advanced Terraform experience.
• Strong understanding of cloud networking, landing zones, and governance.
• Hands-on experience with Kubernetes, GKE, and/or OpenShift.
• Experience with HashiCorp Vault, IAM, and secrets management.
• Understanding of MLOps, LLMOps, GenAI, LLM, and RAG architectures.
• Experience with observability, SRE, SLOs, internal developer platforms, cloud security, and policy-as-code.
• Strong scripting and automation skills; Python is preferred.
• Troubleshooting, communication, and collaboration skills.

Preferred experience

• Experience supporting GenAI/LLM production workloads.
• Experience with Arize AI or similar AI/ML observability platforms.
• Experience in hybrid or multi-cloud environments.
• Experience integrating LLM APIs, inference services, and RAG platforms.
• Experience working with data science and ML engineering teams.

Rate

• The stated rate is $50 to $55 per hour on W2.

Work arrangement

• Hybrid remote work in Iselin, New Jersey.