The 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.
Responsibilities
- 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.
- 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.
Core Technical Skills
- 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.
Preferred Qualifications
- GCP Professional ML Engineer or Cloud Architect certification.
- Experience with Looker or other operational dashboards.
AI/ML Engineer employer: ThoughtStorm
At ThoughtStorm, we pride ourselves on fostering a collaborative and innovative work culture that empowers our employees to excel in their roles. Located in the vibrant city of Toronto, we offer competitive benefits, continuous learning opportunities, and a commitment to professional growth, making us an ideal employer for those looking to make a meaningful impact in the tech industry.
