Azure Google AI​/ML Associate Platform Engineer

Position: Azure Google AI/ML Associate Platform Engineer I
Work Location:

Toronto, Ontario, Canada

Hours:

37.5

Line of Business:

Technology Solutions Pay Details:$81,600 - $115,200 CADTD is committed to providing fair and equitable compensation opportunities to all colleagues. Growth opportunities and skill development are defining features of the colleague experience  compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The base pay actually offered may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational needs.

As a candidate, you are encouraged to ask compensation related questions and have an open dialogue with your recruiter who can provide you more specific details for this role.

Job Description:

Our mission is to advance TD by enabling secure, scalable Azure and Google Cloud AI/ML capabilities across the enterprise to solve business problems with AI and deliver solutions to customers faster. We are a multi-cloud Platform Engineering team, building Infrastructure as Code (IaC), automated testing frameworks, and self-service tooling that allow lines of business to consume cloud services safely within a financial services environment.

As an associate Platform Engineer, you will learn and help Senior platform engineers design the Terraform modules, the Git Hub Actions pipelines, the security model, and the operational story for an entire cloud service. You will help with design, developing and testing.

You will work across a modern, opinionated platform stack:
Terraform and the native Azure/Google SDKs for IaC;
Python for automation and tooling;
Git Hub Actions for CI/CD; and a security-first toolchain spanning RBAC/IAM, Active Directory, Ping Federate, PKI, Key Vault / Secret Manager, and policy-as-code. The services in scope include Azure OpenAI, Azure AI Foundry, Azure AI Search, Azure Bot Service, Azure ML, and Google Vertex AI.KEY

ACCOUNTABILITIES Help with end-to-end delivery of Infrastructure as Code for one or more cloud AI services - Azure OpenAI, Azure AI Foundry, Azure AI Search, Azure Bot Service, Azure ML, or Google Vertex AI - from Terraform module design and pipeline plumbing through to production rollout and Day-2 operations.

Design platform abstractions that let application teams self-serve safely: opinionated Terraform modules, Git Hub Actions reusable workflows, Python CLIs and SDKs, and golden paths that bake in security, networking, and observability.

Embed security and compliance into the platform: RBAC/IAM design, federated identity (Azure AD/Entra, Google Cloud Identity, AD, Ping Federate), private networking, certificate/PKI lifecycle, secrets management, policy-as-code, and audit/evidence workflows.

Build patterns for safe consumption of generative AI - private endpoints for Azure OpenAI and Vertex AI, RAG architectures with Azure AI Search and vector stores, model gateway / routing layers, content filtering and prompt-shield controls, MLOps/LLMOps pipelines, and evaluation tooling.

EXPERIENCE & EDUCATION1+ years of cloud platform or infrastructure engineering experience, with IaC delivery on at least one of Azure or Google Cloud.

Terraform - module design, composition, work spaces, testing, drift management, and pipeline patterns.

Python for platform work: SDK integrations, CLI/tooling, automated testing, packaging, observability hooks.

Experience with Git Hub Actions (or comparable CI/CD) including reusable workflows, environments, OIDC-based cloud auth, deployment gating, and secret management.

Demonstrable experience deploying and operating AI/ML services on Azure (Azure OpenAI, Azure AI Foundry, Azure AI Search, Azure Bot Service, Azure ML) and/or Google Cloud (Vertex AI, Big Query, Pub/Sub).Hands-on understanding of cloud networking at depth: VNets/VPCs, NSGs/firewall rules, hub-spoke and shared VPC patterns, private endpoints, Express Route, DNS, and routing across hybrid topologies.

Security engineering instincts: RBAC/IAM design, identity federation (Azure AD/Entra, Google Cloud Identity, AD, Ping Federate), Key Vault / Secret Manager,…