Summary
✨ AI‑Generated
A senior Cloud & AI Engineer is sought to architect and deliver secure, robust cloud foundations and AI enablement solutions. The role combines hands-on cloud infrastructure, platform engineering, DevOps, and architecture across major public-cloud environments, supporting modernization and responsible adoption of emerging AI capabilities.
Highlights
Senior-level opportunity focused on secure, robust cloud and AI enablement. The role offers the chance to architect cloud foundations, support application modernization, enable emerging AI capabilities, and influence enterprise-scale cloud adoption.
Description
Cloud & AI Engineer
Job Title: Cloud & AI Engineer
Experience Level: Level 3 (senior): 5-7 years
12+ month
Location: Montreal (Day 1 onboarding onsite/in office presence 3x/week)
The Cloud Business Unit Enablement team is responsible for accelerating public cloud adoption throughout ***.
This is a global, multi-discipline team responsible for architecting and delivering secure, robust, and innovative cloud and AI enablement solutions which enable development teams to build and deploy new applications, modernize existing workloads, and safely adopt emerging AI capabilities across the public cloud.
Qualified Candidate MUST have:
5 to 7 years of overall IT industry experience, with strong hands-on engineering background in cloud infrastructure, platform engineering, DevOps, or related disciplines.
3 to 5 years of proven experience in cloud technologies across Azure and AWS.
Strong knowledge of Azure and AWS Landing Zone architecture, cloud foundations, account or subscription structures, governance, and security controls.
Hands-on experience with Terraform module development, GitHub, GitHub Actions, and CI/CD automation.
Practical experience with Kubernetes, containerized workloads, AKS, and EKS.
Strong Python programming skills for automation, AI application development, API integrations, and platform engineering use cases.
Solid understanding of on-premises to cloud connectivity including VPN, ExpressRoute, Direct Connect, routing, DNS, firewalls, private endpoints, and enterprise network segmentation.
Hands-on experience with core Azure and AWS services used to support application, data, platform, and AI workloads.
Deep understanding of enterprise networking, identity, access management, observability, and security patterns in public cloud.
Strong understanding of LLM fundamentals, generative AI concepts, embeddings, vector search, RAG patterns, model limitations, and responsible AI considerations.
Practical experience with prompt engineering, prompt optimization, structured outputs, prompt chaining, and AI workflow design.
Hands-on exposure to AI agent development, agent orchestration, AI agent state management, and AI evaluation harnesses.
Experience with AI development frameworks and tools such as LangGraph, LangChain, Claude Code SDK, OpenAI ADK, or similar technologies.
Ability to collaborate effectively with security, networking, infrastructure, vendor, product, and application development teams in a large enterprise environment.
Qualified Candidate NICE to have:
Experience deploying applications and platforms using resilient, highly available, multi-region, and disaster recovery aware architectures.
Experience with Azure AI Foundry, Azure OpenAI, AWS Bedrock, Anthropic Claude, OpenAI, enterprise AI gateways, or internal AI enablement platforms.
Experience building AI copilots, intelligent assistants, agentic automation workflows, enterprise knowledge assistants, or AI-powered self-service platforms.
Familiarity with Model Context Protocol (MCP), AI gateways, vector databases, semantic search, RAG pipelines, and enterprise knowledge integrations.
Valid Azure and/or AWS certifications, preferably beyond a single fundamentals exam.
Experience working in financial services, regulated environments, or large-scale enterprise technology organizations.
