Vertex AIOps Platform Engineer (GCP)

Overview

The Lead AIOps Engineer is responsible for architecting, provisioning, and operationalizing multi-environment AI platforms on Google Cloud (Sandbox, Dev, Prod). The role includes cloud environment setup, IAM governance, CI/CD pipeline development, AIOps automation, drift detection, lifecycle process design, documentation, and alignment with broader enterprise platforms. This is a hands-on technical leadership position.

Responsibilities

Environment Provisioning

• Conduct workshops to gather GCP environment requirements.
• Design cloud architecture including VPC, IAM, subnetting, quotas, endpoints, and security controls.
• Lead the provisioning of Sandbox, Dev, and Prod GCP projects using Terraform.
• Oversee API enablement, configuration, and validation testing.

Role Definitions & IAM Governance

• Define IAM roles for AI platform users (Owner, Support, ML Engineer, Viewer).
• Create IAM matrices, RACI charts, and detailed access control documentation.
• Ensure least-privilege access policies across Vertex AI and GCP services.
• Coordinate reviews and approvals with security and architecture teams.

AIOps Framework Development

• Design and implement drift detection, anomaly monitoring, canary releases, automated rollback, and observability components.
• Build reusable CI/CD pipelines using Vertex Pipelines and Cloud Build.
• Develop SOPs, diagrams, runbooks, and the full AIOps operations playbook.
• Execute and validate synthetic drift, monitoring, and pipeline test scenarios.

Lifecycle Processes

• Define the complete ML lifecycle from environment setup through deployment, monitoring, retraining triggers, and retirement.
• Integrate lifecycle processes within CI/CD and AIOps automation.
• Document all lifecycle flows in Confluence and conduct validation sessions.

Resource Planning & Cost Modelling

• Develop team structure, roles, and support plans.
• Build cost and usage models using GCP calculators and automation scripts.
• Prepare development and production usage forecasts and long-term TCO estimates.

Alignment Analysis

• Assess synergy with existing enterprise initiatives (Data Lake, Billing, Cloud Migration, Security).
• Document dependencies, risks, and overlapping components.
• Produce final recommendations and alignment reports.

Core Technical Skills

• Strong expertise in Google Cloud Platform: Vertex AI, IAM, VPC, Cloud Build, Cloud Run, Cloud Functions, Pub/Sub.
• Deep experience with Terraform and Infrastructure as Code workflows.
• Practical experience with AIOps and MLOps frameworks.
• Proficient in Python for automation and monitoring jobs.
• Experience designing and operating CI/CD pipelines for ML workloads.
• Knowledge of observability tools such as Cloud Monitoring, Logging, and OpenTelemetry.

Soft Skills

• Strong client-facing and stakeholder engagement abilities.
• Experience leading engineering teams and driving architectural decisions.
• Excellent documentation and presentation skills.
• Ability to guide cross-functional teams through complex technical implementations.

• GCP Professional ML Engineer or Cloud Architect certification.
• Experience with Looker or other operational dashboards.
• Background in ML engineering or SRE.