| Location | North Carolina, NC |
Role Summary The role focuses on GKE platform engineering, infrastructure automation, security, and reliability, with working knowledge of GenAI services and guardrails to support GenAI workloads hosted on the platform. This is not a GenAI model-building role-instead, the engineer ensures that GenAI and non‑GenAI workloads run securely, reliably, and compliantly on GKE. Key Responsibilities GKE Platform Engineering Design, deploy, and manage Google Kubernetes Engine (GKE) clusters for enterprise workloads. Build and maintain shared Kubernetes platforms supporting multiple application teams. Implement cluster-level capabilities such as: Networking and ingress Autoscaling and capacity planning High availability and disaster recovery Standardize GKE configurations following enterprise and security best practices. Infrastructure as Code (IaC) Provision and manage GCP infrastructure using Terraform. Automate creation of: GKE clusters Networking, IAM, and service accounts Supporting platform services Develop reusable Terraform modules and enforce IaC standards. Cloud & Platform Operations Operate and support production-grade GCP environments. Implement monitoring, logging, and ing for GKE clusters and workloads. Troubleshoot cluster, networking, and workload-level issues. Optimize platform reliability, performance, and cost. Security & Guardrails (GenAI-Aware Platform) Implement and enforce GCP security guardrails, including: Model Armor Sensitive Data Protection (SDP) Ensure platform compliance with: Enterprise security standards Data privacy and access controls Support secure hosting of GenAI workloads on GKE, without owning model development. GenAI Platform Enablement (Awareness-Level) Maintain working knowledge of GCP GenAI services (e.g., Vertex AI) from a platform perspective. Enable teams to deploy GenAI-enabled applications on GKE securely. Understand GenAI concepts such as: Inference workflows Data sensitivity risks Responsible AI constraints Partner with application and AI teams to ensure GenAI workloads meet platform, security, and compliance requirements. Automation & Scripting Use Python for: Platform automation Operational tooling Integration scripts Support CI/CD pipelines for platform and application deployments. Required Skills & Experience Core Platform Skills Strong hands-on experience with GCP / Azure or OCP (Openshift) platform Deep experience with Google Kubernetes Engine (GKE). Solid working knowledge of: Kubernetes concepts (pods, services, ingress, autoscaling) Cluster operations and troubleshooting Experience supporting large-scale, multi-team Kubernetes environments. Infrastructure & Automation Proven experience using Terraform for IaC on GCP / Azure or OCP (Openshift) platform. Proficiency in Python for automation and scripting. CI/CD and Git-based workflows. Security & Governance Experience implementing GCP security and governance controls. Working knowledge of GCP Guardrails, including: Model Armor Sensitive Data Protection (SDP) Strong understanding of IAM, networking, and least-privilege access. GenAI Conceptual Understanding (Platform-Level) Good understanding of Generative AI concepts, including: LLM lifecycle basics Inference vs. training Data privacy and security considerations Ability to support GenAI workloads from a platform and governance standpoint, not application logic.