Cloud Engineer

''•Toronto, ON

16d•CA$85,500 - CA$114,000•Hybrid

About The Position

The Cloud Engineer is responsible for engineering, implementing, documenting, and delivering robust cloud and hybrid platforms and services on Microsoft Azure, with an emphasis beyond foundational landing zone design. This role provides technical guidance to business stakeholders, ensuring technical requirements are understood and met, and supports the implementation and delivery of secure, scalable, and cost-effective cloud services. Preference will be given to candidates with demonstrated hands-on experience across the Azure ecosystem, Microsoft 365, Microsoft Copilot, and Azure AI/foundational services to support emerging business needs.

Requirements

University degree in Computer Science (or related field) or equivalent work experience.
Minimum 8+ years as a Cloud Engineer supporting complex enterprise environments.
Highly seasoned, deep experience across Microsoft Azure services (compute, network, identity, security, data, integration, monitoring, cost management).
Demonstrated experience with Microsoft 365 service architecture and modern workplace capabilities.
Strong preference for candidates with demonstrated experience delivering or enabling cloud engineering capabilities involving Microsoft Copilot, Azure AI services, and related enterprise AI capabilities, including governance, enablement, and adoption considerations.
Designed and implemented MCP-compatible services to extend Microsoft Copilot capabilities with external data sources
Integrated Copilot with enterprise systems (CRM, ERP, internal APIs) using MCP server architecture
Proven track record implementing and maintaining engineering standards, best practices, and documentation.
Strong process, time management, stakeholder engagement, and communication skills.

Nice To Haves

Microsoft Certified: DevOps Engineer Expert
Microsoft Certified: Azure Security Engineer Associate
Microsoft Certified: Azure Network Engineer Associate
Microsoft Certified: Security, Compliance, and Identity Fundamentals
Microsoft Certified: Azure AI Engineer Associate (strongly preferred, especially for candidates with Microsoft Copilot and enterprise AI implementation experience)

Responsibilities

Engineer and implement cloud and hybrid solutions across compute, networking, identity, security, data, and integration services—beyond landing zone patterns.
Engineer and support migration and modernization initiatives for applications and data to Azure; assess current systems, plan implementation approaches, and ensure secure, efficient transitions.
Partner with stakeholders to identify and deliver cloud engineering use cases leveraging Microsoft 365 and Microsoft Copilot capabilities; translate productivity and AI opportunities into actionable technical designs.
Implement cloud engineering patterns that incorporate Azure AI and foundational services where appropriate (e.g., AI-enablement, platform capabilities, governance considerations) to support upcoming initiatives.
Implement cloud engineering solutions in alignment with organizational requirements for data governance, security, compliance, resiliency, performance, and cost.
Create white papers and presentations for leadership, communicate the value of cloud, M365, and AI/Copilot capabilities, and support stakeholder understanding of engineering solutions and platform capabilities.
Mentor and challenge team members; share skills and system knowledge through formal and informal channels; communicate effectively with project teams and management.
Stay current on industry technologies, Azure platform changes, M365/Copilot evolution, and best practices; contribute to reusable engineering patterns and implementation guidance.
Ensure solutions and migrations are designed to be AI‑ready and aligned to McCain’s enterprise AI platform, governance, and observability standards.
Partner with the AI Platform team to ensure workloads integrate with approved Copilot, agent, and AI service patterns.
Ensure implemented solutions maintain a clear separation between systems of record and AI-augmented experiences.
Implement end-to-end AI orchestration solutions, engineer AI workflows, and apply prompt engineering best practices.
Implement logging, metrics, tracing, and evaluation requirements to support monitoring, reliability, and governance.