Cloud & AI Engineer

Job Title: Cloud & AI Engineer
Experience Level: Level 3 (senior): 5-7 years
Job Level: FTC
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.

Job Responsibilities:
The Cloud & AI Engineer will be part of the Cloud Business Enablement squad and will be responsible for designing, building, and maintaining enterprise-scale multi-cloud infrastructure across Azure and AWS, while enabling cloud-native AI solutions and agentic AI platforms. The role requires a strong understanding of Landing Zone architecture, cloud security controls, enterprise networking, Kubernetes, Terraform, automation pipelines, LLM fundamentals, prompt engineering, and AI agent development.

Role Overview & Expectations:

• Serve as a hands-on engineer implementing Landing Zones, multi-cloud infrastructure, network connectivity, cloud security patterns, and shared platform services across Azure and AWS.
• Write reusable Terraform modules and enforce Infrastructure as Code best practices using GitHub and GitHub Actions.
• Act as a subject matter expert on hybrid connectivity, cloud governance, platform observability, identity, and enterprise security frameworks.
• Provide technical guidance to application development teams on cloud-native designs, Kubernetes adoption, resilient architecture patterns, and AI-enabled workload integration.
• Develop Python-based automation and integration tooling to improve self-service, operational efficiency, compliance, and platform reliability.
• Design and implement AI agents, LLM-powered workflows, prompt engineering patterns, state management, and evaluation frameworks for enterprise use cases.
• Ensure observability and monitoring through integrations with AWS CloudWatch, Azure Log Analytics Workspace, Splunk, Datadog, Akamai, F5, and related enterprise tools.
• Deliver secure, resilient, and highly available architectures that meet enterprise SLA, compliance, and operational requirements.
• Collaborate with security, networking, infrastructure, product, vendor, and application teams to continuously improve cloud operations, automation, governance, and AI enablement.
• Stay current with emerging cloud, AI, agentic workflow, Kubernetes, and automation technologies and recommend improvements to enhance efficiency, performance, security, and developer experience.

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.

*//EEO Employer: Minorities/ Females/ Disabled/ Veterans/ Gender Identity/ Sexual Orientation//*