Cloud Platform Engineer

Cloud Platform Engineer Location: Charlotte, NC Rate :: $72/hr on w2 Position ID :: 102602-91-4 , 102602-91-3 No.of positions :: 2 Key Skills: Must-Have Skills (Mandatory): GCP, Azure (multi-cloud preferred) Terraform (strong hands-on IaC) Cloud Networking & Hybrid Connectivity (VPN, VPC/VNet peering, private endpoints) Landing Zones & Cloud Governance (Org Policies, guardrails) Kubernetes (GKE), OpenShift (OCP) Platform Engineering / Internal Developer Platforms Observability (monitoring, logging, tracing) SRE concepts (SLOs, SLIs, reliability engineering) Python (automation) HashiCorp Vault (secrets management) GenAI / Advanced Skills (Strong Preferred): GenAI Platforms / LLMs RAG (Retrieval Augmented Generation) MLOps / LLMOps pipelines Key Responsibilities (Keywords for Search): Build enterprise cloud platforms (GCP + Azure) Implement Terraform-based reusable modules Design landing zones & governance frameworks Enable hybrid/multi-cloud connectivity Manage Kubernetes platforms (GKE/OCP) Build Internal Developer Portals (self-service infra) Define SLOs, reliability patterns, observability Support GenAI/LLM workloads and platform enablement GCP · Azure · Terraform · Cloud Networking · Landing Zones · Org Policy / Governance · HashiCorp Vault · Hybrid Connectivity · Kubernetes · GKE · OpenShift (OCP) · Platform Engineering · Observability · SRE / SLOs · Python · Internal Developer Portals · GenAI Platforms · LLMs · RAG · MLOps/LLMOps Responsibilities: · Design, build, and operate secure, scalable GCP and OpenShift (OCP/GKE) platforms to support deployment of GenAI models, LLMs, and RAG workloads. · Provision and manage cloud infrastructure using Terraform, including landing zones, networking, org policies, and hybrid connectivity across GCP and Azure. · Enable MLOps/LLMOps pipelines for model deployment, monitoring, and lifecycle management, integrating Arize AI and GenAI platforms. · Implement platform engineering best practices, including Kubernetes-based abstractions, internal developer portals, and self-service environments. · Ensure platform security, governance, and secrets management using HashiCorp Vault, IAM, and policy-as-code. · Establish observability, SLOs, and SRE practices to ensure reliability and performance of GenAI and platform services. · Collaborate with data scientists, ML engineers, and application teams to onboard new LLMs, APIs, and inference services efficiently.