Group Technical Architect

Job Summary

The Google Cloud AI DevOps Engineer (8+ years) is responsible for designing, building, and managing end‑to‑end pipelines and infrastructure on Google Cloud Platform (GCP). This role combines deep cloud engineering expertise with DevOps best practices to enable scalable CI/CD, automated deployments, and robust monitoring for AI/ML and data-driven platforms. The engineer collaborates closely with data scientists, platform engineering, and infrastructure teams to deliver secure, reliable, and efficient GCP-based solutions.

Key Responsibilities

Key Responsibilities:

• Design, implement, and maintain automated build and deployment pipelines using GCP services such as Cloud Build, Cloud Functions, GKE, and GCE.

• Develop and manage infrastructure using IaC tools (Terraform, Cloud Deploy, Cloud Build, Jenkins, Packer, Terragrunt) to ensure consistent and scalable deployments.

• Create and optimize container images and manage container registries.

• Integrate and manage DevOps tools such as Jenkins, GitHub Actions, Bitbucket Pipelines, ArgoCD, and Tekton.

• Implement monitoring and logging using Cloud Operations, Prometheus, and Grafana.

• Apply automation and security best practices ensuring reproducibility, scalability, and compliance.

• Manage source control repositories (GitHub, Bitbucket) including branching, code reviews, and releases.

• Provide technical guidance, documentation, and best-practice enablement.

Skill Requirements

Required Skills & Expertise:

• Experience building pipelines and infrastructure on GCP using tools such as Vertex AI, GKE, Cloud Build, and Cloud Functions.

• Expertise in DevOps methodologies and CI/CD tools (Jenkins, GitHub Actions, Bitbucket Pipelines, ArgoCD, Tekton).

• Deep knowledge of Docker, Dockerfiles, and container registries.

• Hands-on experience with Terraform, Deployment Manager, and scripting (Python, Bash).

• Strong Git-based workflow experience.

• Monitoring/logging experience using Cloud Operations, Prometheus, Grafana.

• Strong collaboration skills with data science and platform engineering teams.

• Understanding of cloud security, IAM, and governance.

• Experience tuning and scaling AI workloads on GCP.

• Preferred: Google Cloud DevOps or AI Engineer certifications.

Other Requirements

Qualifications & Certifications:

• Bachelor’s degree in IT, Engineering, or a related field; MBA/management qualification is a plus.

• GCP Professional DevOps Engineer certification (required).

• GCP Professional Cloud Architect certification (preferred).

• Terraform Associate certification.