Senior Azure DevOps / AI Platform DevOps
- Ottawa, ON, Canada
Contract Period: 12 months with potential to extend or convert Pay Rate: Starting from $76 per hour Location: Ottawa, ON Location Type: Hybrid Business Hours: Monday-Friday, Core business hours · Contract
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Job details
- Pay
- Contract Period: 12 months with potential to extend or convert Pay Rate: Starting from $76 per hour Location: Ottawa, ON Location Type: Hybrid Business Hours: Monday-Friday, Core business hours
- Job type
- Contract
- Apply by
- Experience
- Minimum 5 years of relevant experience in DevOps or Cloud Engineering roles
- Posting language
- English
- Posted
Original job description
Introduction:
Robertson is seeking a skilled Senior Azure DevOps / AI Platform DevOps to join our client in support of an existing vacancy.
Contract Period: 12 months with potential to extend or convert
Pay Rate: Starting from $76 per hour
Location: Ottawa, ON
Location Type: Hybrid
Business Hours: Monday-Friday, Core business hours
Job Responsibilities:
- Implement and optimize DevOps practices within an Azure cloud environment, ensuring security, scalability, resilience, and reliability
- Design, build, and maintain robust GitLab CI/CD pipelines to accelerate software delivery, infrastructure automation, and production deployment
- Support the deployment and operation of containerized workloads using Kubernetes and/or OpenShift, applying platform engineering and operational best practices
- Contribute to the design and delivery of AI/Agentic AI DevOps capabilities, enabling secure, production-ready deployment of AI workloads and supporting associated infrastructure and controls
- Drive automation for infrastructure provisioning, configuration, and deployment using Infrastructure as Code principles and repeatable engineering practices
- Collaborate closely with development, operations, security, and enterprise platform teams to troubleshoot and resolve complex live performance, stability, and integration issues, while implementing preventative improvements
- Support release planning, deployment scheduling, and operational readiness for cloud-hosted and AI-enabled applications
- Provide technical guidance and recommendations to engineering teams based on operational analysis, observability data, and platform monitoring insights
- Ensure solutions align with enterprise requirements for security, authentication, access control, governance, and integration with corporate systems
- Stay current with emerging DevOps, Azure, Kubernetes, and AI platform technologies, sharing knowledge and mentoring team members where appropriate
- Proven experience deploying, managing, and optimizing applications and platforms in Microsoft Azure
- Strong hands-on experience with Kubernetes, including cluster operations, workload deployment, and platform best practices
- Strong understanding of networking and security, particularly in cloud and containerized environments
- Demonstrated success designing and maintaining GitLab CI/CD pipelines for enterprise-scale applications and services
- Strong scripting and automation capability using Python, with Bash or similar scripting also desirable
- Experience integrating solutions with enterprise systems, including identity, authentication, and access management components
- Strong troubleshooting skills with a proactive approach to reliability, operational support, and continuous improvement
- Ability to communicate effectively with both technical and non-technical stakeholders
- Desirable experience supporting AI/ML or Agentic AI workloads in production
- Minimum 5 years of relevant experience in DevOps or Cloud Engineering roles
- Significant expertise in Microsoft Azure cloud services and deployments
- Must have expertise in AI workflow (experience supporting AI/ML or Agentic AI workloads in production)
- A proven track record of building and supporting CI/CD pipelines within enterprise environments
- Strong scripting and automation skills, with a specific focus on Python
- Hands-on coding experience with Terr grunt and Terraform
- Experience integrating automated testing into the deployment lifecycle
- Experience implementing and managing comprehensive DevOps processes and best practices
Background screening is required as part of the onboarding process. The type of screening required (criminal, credit, or other verifications) will vary based on the position and client requirements.
We use AI technology as part of our application review process to assist with screening and assessment. All applications are also reviewed by our recruitment team.
More about this job
Responsibilities
Build and optimize Azure DevOps practices, GitLab CI/CD pipelines, infrastructure automation, and Kubernetes or OpenShift operations. Support secure production deployment of AI workloads, troubleshoot platform issues, and collaborate with engineering, operations, security, and enterprise platform teams to improve reliability and readiness.
Requirements
Requires at least five years of DevOps or cloud engineering experience, significant Microsoft Azure expertise, and experience supporting AI/ML or Agentic AI workloads in production. Candidates must have enterprise CI/CD experience, strong Python automation skills, hands-on Terragrunt and Terraform coding experience, and experience with automated testing and DevOps best practices.
Skills
- Microsoft Azure
- DevOps
- GitLab CI/CD
- Kubernetes
- OpenShift
- AI/ML Workloads
- Agentic AI
- Python
- Bash
- Terraform
- Terragrunt
- Infrastructure as Code
- Cloud Networking
- Cloud Security
- Identity and Access Management
- Automated Testing
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Engineering
- Finance & Accounting
Keywords
- Microsoft Azure
- Azure Cloud Services
- DevOps
- Cloud Engineering
- GitLab CI/CD
- Kubernetes
- OpenShift
- AI/ML
- Agentic AI
- Python
- Bash
- Terragrunt
- Terraform
- Infrastructure as Code
- Automated Testing
- Cloud Networking
- Cloud Security
- Identity and Access Management
- Authentication
- Access Control
- Platform Engineering
- Containerized Workloads
- Infrastructure Automation
- Configuration Management
- Observability
- Production Deployment
- Operational Readiness
- Reliability Engineering
- Enterprise Systems
- Financial Services