GCP DevOps Engineer

Role Overview:


This role is for an experienced Engineer specializing in cloud infrastructure, automation, and operations, with a primary focus on Kubernetes and Google Cloud Platform (GCP). The successful candidate will be instrumental in implementing Infrastructure as Code (IaC), managing robust CI/CD pipelines, and providing critical support for production systems. A significant aspect of the role involves applying AI/ML concepts and AIOps practices to enhance operational efficiency and incident response.


Key Responsibilities:

• Manage incidents, provide on-call support, and perform production triage to ensure system stability.
• Implement hands-on automation solutions and develop comprehensive CI/CD pipelines.
• Apply strong understanding of AI/ML concepts and AIOps practices, including model lifecycle management, monitoring, and AI-driven alerting.
• Support and operate ML/AI platforms or pipelines (MLOps) as a preferred responsibility.
• Integrate AI-driven automation into existing monitoring and incident response frameworks (preferred).

Required Skills:

• Strong experience with Kubernetes and Google Cloud Platform (GKE).
• Strong experience in Infrastructure as Code (IaC) using Terraform, Helm, and GitHub Actions.
• Proficiency in programming languages such as Python, Ansible, and Node.js.
• Strong experience with the Prometheus and Grafana observability stack.
• Solid understanding of Linux systems and networking fundamentals.
• Proven experience in incident management, on-call support, and production triage.
• Hands-on experience with automation and CI/CD pipelines.
• Strong understanding of AI/ML concepts and AIOps practices (model lifecycle, monitoring, or AI-driven alerting).

Qualifications:

• 8-10 years of relevant experience.

Preferred Skills:

• Google Cloud Architect Certification.
• Certified Kubernetes Administrator (CKA).
• Experience in Java/J2EE and Spring Boot.
• Experience supporting or operating ML/AI platforms or pipelines (MLOps).
• Exposure to AIOps tools, anomaly detection, or predictive analytics systems.
• Experience with large-scale distributed systems and microservices architecture.
• Experience with GPU-based workloads or ML infrastructure on GCP.
• Knowledge of Kubeflow, Vertex AI, or ML pipelines.
• Experience integrating AI-driven automation into monitoring and incident response.