DevOps Engineer

MLOps / DevOps Engineer

Remote – Canada Preferred (Vancouver Ideal)

Details: Full-Time | $120-140K CAD depending on experience

*Must be authorized to work in Canada. Does not offer sponsorship. Not using outside recruiters.*

Our client is hiring an MLOps / DevOps Engineer to join a growing infrastructure and data platform team supporting cloud infrastructure, CI/CD systems, Kubernetes environments, and emerging ML infrastructure initiatives.

This is an excellent opportunity for someone with a strong DevOps foundation who wants to expand deeper into MLOps, AI infrastructure, GPU workloads, and machine learning deployment workflows while working alongside experienced engineers and data teams.

What You’ll Do:

• Support and improve Kubernetes (EKS), AWS, and cloud-native infrastructure environments.
• Build and optimize CI/CD pipelines, deployment workflows, and infrastructure automation.
• Help support ML infrastructure, model deployment workflows, and data platform systems.
• Work with Infrastructure as Code tools like Terraform and CloudFormation.
• Improve monitoring, observability, and platform reliability using tools like Prometheus, Grafana, and CloudWatch.
• Collaborate closely with platform, product, and data teams to improve scalability, automation, and developer experience.
• Support GPU-enabled workloads, SageMaker environments, and production ML workflows.

What We’re Looking For:

• 2-5 years of DevOps, Cloud Infrastructure, or Platform Engineering experience.
• Experience with Kubernetes, Docker, AWS, and CI/CD pipelines.
• Hands-on experience with Terraform or similar Infrastructure as Code tooling.
• Experience supporting cloud-native applications and microservices environments.
• Exposure to ML infrastructure, SageMaker, data pipelines, or MLOps workflows.
• Strong troubleshooting, automation, and operational mindset.
• Excellent communication skills and a collaborative attitude.

Nice to Have:

• Experience with SageMaker, MLflow, Kubeflow, Airflow, or Argo Workflows.
• Experience supporting GPU-enabled Kubernetes workloads.
• Exposure to LLM or AI infrastructure deployments.
• Experience with monitoring and observability tooling.
• Interest in growing deeper into AI/ML infrastructure and platform engineering.

Why Join?

• Opportunity to grow into MLOps and AI infrastructure.
• Work alongside experienced infrastructure and data engineering leaders.
• High-impact role with ownership and mentorship.
• Remote-first flexibility and collaborative engineering culture.
• Competitive compensation and strong long-term growth opportunity.

Interested in helping build modern cloud and ML infrastructure in a fast-growing environment? Apply now.