Kubernetes/Google Cloud Platform Engineer

Duration: 12 Months (Contract to perm)
Pay Rate: 50/HR
Work Mode: Hybrid
Interview Type:  Not Mentioned 

Ropes Test: Yes
Location: 

Scottsdale, Arizona 85260

Position Overview

We are seeking a Kubernetes / Google Cloud Platform Engineer with deep hands-on expertise in Google Cloud Platform, infrastructure automation, and modern observability stacks. In this role, you will build and maintain resilient cloud infrastructure, drive CI/CD and IaC best practices, support production systems through effective incident triage, and help integrate AI/ML concepts and AIOps into operational workflows.

Required Qualifications

• Cloud & Platform Engineering: Strong experience with Google Cloud Platform (Google Cloud Platform) and Google Kubernetes Engine (GKE).

• Infrastructure as Code & CI/CD: Strong experience in IaC using Terraform, Helm chart management, and CI/CD automation with GitHub Actions.

• Programming & Scripting: Proficiency in Python, Ansible, and Node.js for automation, integration, and tooling.

• Observability & Monitoring: Strong experience with the Prometheus and Grafana observability stack.

• Core Systems & Networking: Solid understanding of Linux systems administration and networking fundamentals.

• Operations & Incident Response: Proven experience in incident management, on-call support, production triage, and hands-on automation for CI/CD pipelines.

• AIOps & AI Concepts: Strong understanding of AI/ML concepts and AIOps practices, including model lifecycle, AI/ML monitoring, or AI-driven alerting.

Preferred Qualifications

• Certifications: Google Cloud Certified Professional Cloud Architect and/or Certified Kubernetes Administrator (CKA).

• Software Engineering: Experience in Java/J2EE and Spring Boot applications.

• MLOps & AI Infrastructure: Experience supporting or operating ML/AI platforms, pipelines (MLOps), GPU-based workloads, or ML infrastructure on Google Cloud Platform.

• ML Platforms & Frameworks: Knowledge of Kubeflow, Vertex AI, or cloud-native ML pipelines.

• Advanced AIOps & Automation: Exposure to AIOps tools, anomaly detection, predictive analytics systems, and integrating AI-driven automation directly into monitoring and incident response.

• Distributed Systems: Experience working with large-scale distributed systems and microservices architecture.