Summary

- Duration: 12 months

- Location: Montreal

- Work Mode: Day 1 onboarding onsite/in office presence 3x/week

Responsibilities

- Design, build, and maintain enterprise-scale multi-cloud infrastructure across Azure and AWS.

- Enable cloud-native AI solutions and agentic AI platforms.

- Ensure cloud security controls, enterprise networking, Kubernetes, and Terraform automation pipelines are in place.

- Collaborate with global teams to connect complex applications from on-premises to public cloud platforms.

- Partner with product management, platform engineering, security, networking, vendors, and application development teams.

- Develop, maintain, and enhance reusable Terraform modules and cloud infrastructure blueprints.

- Implement and manage Kubernetes platforms including AKS and EKS.

- Deploy, secure, and operate Azure and AWS services.

- Design, develop, and deploy AI agents and autonomous workflows using modern AI frameworks.

Requirements

- Minimum 5 years of overall IT industry experience with a strong hands-on engineering background in cloud infrastructure, platform engineering, or DevOps.

- Minimum 3 years of experience in cloud technologies across Azure and AWS.

- Strong knowledge of Azure and AWS Landing Zone architecture and cloud security controls.





- Hands-on experience with Terraform, GitHub, GitHub Actions, and CI/CD automation.

- Practical experience with Kubernetes, containerized workloads, AKS, and EKS.

- Robust Python programming skills for automation and AI application development.

- Solid understanding of on-premises to cloud connectivity.

- Hands-on experience with core Azure and AWS services.

- Deep understanding of enterprise networking, identity, access management, observability, and security patterns.

- Strong understanding of LLM fundamentals and generative AI concepts.

Preferred Skills

- Experience deploying applications using resilient, highly available, multi-region architectures.

- Experience with Azure AI Foundry, Azure OpenAI, AWS Bedrock, or similar platforms.

- Experience building AI copilots, intelligent assistants, or AI-powered self-service platforms.

- Familiarity with Model Context Protocol (MCP), AI gateways, vector databases, and semantic search.

- Valid Azure and/or AWS certifications beyond fundamentals.

- Experience working in financial services or large-scale enterprise technology organizations.

This role is for an existing vacancy.

📌 Cloud & AI Engineer (Montreal)
🏢 Axelon Services
📍 Montreal