Cloud AI Platform Engineer – Azure & Google

TD; Toronto; On-site; CAD 154,000 - 200,000; Full time

Job summary

TD in Toronto seeks a Platform Engineer to own end-to-end Infrastructure as Code for cloud AI services, designing Terraform modules, CI/CD pipelines, and security models, while guiding other engineers on quality and reliability.

You'll work with Terraform, Azure and Google Cloud SDKs, Python, GitHub Actions, RBAC/IAM, private endpoints, and MLOps tooling to enable secure, scalable AI capabilities for the enterprise.

Qualifications

• 5+ years of cloud platform or infrastructure engineering experience, with deep IaC delivery on at least one of Azure or Google Cloud.

Responsibilities

• Own end-to-end delivery of Infrastructure as Code for one or more cloud AI services.
• Design platform abstractions that let application teams self-serve safely: Terraform modules, GitHub Actions reusable workflows, Python CLIs and SDKs.
• Lead architectural conversations across Azure and Google Cloud, balancing capability, cost, performance, regulatory fit, and developer experience.
• Embed security and compliance into the platform: RBAC/IAM design, federated identity, private networking, secret management, policy-as-code, and audit/evidence workflows.
• Build patterns for safe consumption of generative AI and lead code review to set IaC quality standards.
• Partner with security, network, risk, and architecture teams to shepherd new capabilities through internal gates.
• Contribute to platform strategy: roadmap shaping, build-vs-buy analyses, vendor evaluations.

Skills

Terraform

Python

GitHub Actions

AI/ML services

Cloud platform engineering

Tools

Azure

Google Cloud

Terraform modules

CI/CD pipelines

Job description

TD in Toronto seeks a Platform Engineer to own end-to-end Infrastructure as Code for cloud AI services, designing Terraform modules, CI/CD pipelines, and security models, while guiding other engineers on quality and reliability.

You'll work with Terraform, Azure and Google Cloud SDKs, Python, GitHub Actions, RBAC/IAM, private endpoints, and MLOps tooling to enable secure, scalable AI capabilities for the enterprise.