Remote Devops Jobs â€ĸ Canada

26 results

Job listings

Devops Engineer - II

Netomi AI 🤖🧠💡

Seeking a highly skilled DevOps Specialist with expertise in Amazon and Azure cloud with Helm charts, Jenkins, and Prometheus to support our growing infrastructure needs. The ideal candidate will have a strong background in managing Kubernetes clusters, automating deployments via Helm, and integrating CI/CD pipelines with Jenkins. This role is critical to ensuring seamless, scalable, and reliable operations for our cloud-native applications.

DevOps Engineer

StackAdapt 📊📈💡
Canada 5w PTO

Optimize critical engineering applications to ensure reliability, scalability and security. Establish tooling and automation processes for infrastructure as code, service recovery and service monitoring. Provide guidance and standards on application capabilities and usage. Develop and maintain infrastructure and security guidelines, procedures, and documentation to streamline operations and promote knowledge sharing.

Principal Platform Engineer

Igloo â„ī¸đŸ§ŠđŸ 

Play a critical role in shaping the core of our engineering platform and work across backend systems, cloud infrastructure, and deployment pipelines to help simplify service boundaries, improve reliability, and enable faster, safer delivery. This is a hands-on role for someone who thrives on creating clarity from complexity and enjoys building foundations that others can build on confidently.

Senior Infrastructure Engineer

Greenhouse 💚🌱đŸŒŋ

The Senior Infrastructure Engineer will architect, develop, and optimize the infrastructure, network, and underpinnings of Backlight products. This role requires ownership of significant infrastructure development ensuring scalability, performance, and security, collaborating with development, product, and security teams to deliver solutions in an agile environment, using GitHub for code and infrastructure management.

DevOps Engineer (MLOps)

Loop 🔄🔁â™ģ
$123,200–$184,800
USD/year

We are seeking a DevOps (MLOps) Engineer to pioneer and mature our machine learning operations capabilities, focusing on building robust infrastructure and deployment pipelines in AWS. This critical role will own the infrastructure underlying all of our productionalized ML models , from deployment to monitoring, fostering seamless collaboration between our machine learning and broader engineering teams.