AI​/ML Engineer

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.

• 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.

• 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.

• 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.

• 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.

• 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 Open Telemetry.

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