Vertex AIOps Platform Engineer

1 week ago

Bangalore, India Jobted IN C2 Full time

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. Requirements: 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. Preferred Qualifications - GCP Professional ML Engineer or Cloud Architect certification. - Experience with Looker or other operational dashboards. - Background in ML engineering or SRE.

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