AI/ML Engineer III

Build and manage Infrastructure as Code (IaC) solutions using Terraform and cloud-native automation frameworks.

Automate provisioning of AI/ML environments, networking, security policies, Vertex AI resources, and cloud services.

Develop reusable automation modules, deployment blueprints, and platform accelerators.

Drive standardization of cloud resources and platform deployments across multiple environments and business units.

Define and implement platform reliability, resiliency, disaster recovery, and high-availability strategies.

Establish SLAs, SLOs, and operational metrics for platform services.

Build proactive monitoring, observability, alerting, and incident management capabilities.

Lead root cause analysis and platform optimization initiatives for critical incidents and service disruptions.

Drive continuous improvement programs focused on platform performance and operational excellence.

Design and govern enterprise CI/CD frameworks supporting AI/ML platform services.

Implement automated testing, validation, security scanning, compliance checks, and release automation.

Integrate GitHub, GitHub Actions, Cloud Build, Jenkins, SonarQube, and DevSecOps controls into platform pipelines.

Promote engineering best practices including code quality, versioning, release governance, and platform standards.

Implement enterprise security controls, governance frameworks, and policy-driven platform automation.

Design and manage access controls, IAM policies, service identities, and secrets management.

Ensure compliance with organizational standards, regulatory requirements, and data governance policies.

Partner with cybersecurity, architecture, and risk teams to establish secure AI/ML platform foundations.

Enable Responsible AI, auditability, model governance, and compliance reporting capabilities.

Lead implementation and governance of Vertex AI services including Workbench, Pipelines, Feature Stores, Model Registry, Endpoints, and GenAI capabilities.

Build and optimize integrations across BigQuery, Cloud Storage, Dataflow, Dataproc, Cloud Run, Cloud Functions, Pub/Sub, and Dataplex.

Drive platform enhancements supporting MLOps, LLMOps, and AI application development.

Enable platform capabilities that simplify onboarding and accelerate AI solution delivery.