What are the responsibilities and job description for the Kubernetes/Google Cloud Platform Engineer (Only W2) position at Trispark Inc?
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.
Salary : $50