Hybrid: 2 days per week in-person at Toronto office preferred

Role Description: We are looking for a highly experienced Senior AWS SageMaker / MLOps Engineer with 8+ years of overall IT experience and strong expertise in building, deploying, and operationalizing Machine Learning solutions on AWS.

The ideal candidate should have hands-on experience with AWS SageMaker, SageMaker Feature Store, MLOps frameworks, and Python development. The resource will be responsible for designing and implementing end-to-end ML pipelines, feature management strategies, model deployment automation, monitoring, and CI/CD integration for enterprise-scale machine learning platforms.

Top 3 Required Skills: AWS SageMaker (Model Development, Training, Deployment, Monitoring)

AWS Sagemaker / MLOPS Engineer Toronto, ON

Hybrid: 2 days per week in-person at Toronto office preferred

Skills: Digital: Python | Digital: Azure Machine Learning (ML) | Digital: Terraform | AI & Gen AI - Products & Tools

Experience Required: 6–8 Years

Role Description: We are looking for a highly experienced Senior AWS SageMaker / MLOps Engineer with 8+ years of overall IT experience and solid expertise in building, deploying, and operationalizing Machine Learning solutions on AWS.





The ideal candidate should have hands‑on experience with AWS SageMaker, SageMaker Feature Store, MLOps frameworks, and Python development. The resource will be responsible for designing and implementing end‑to‑end ML pipelines, feature management strategies, model deployment automation, monitoring, and CI/CD integration for enterprise‑scale machine learning platforms.

Top 3 Required Skills: AWS SageMaker (Model Development, Training, Deployment, Monitoring)

AWS SageMaker Feature Store and Feature Engineering

MLOps and Python Development

Top 3 Preferred Skills: AWS Bedrock / Generative AI

Terraform / CloudFormation

Kubernetes and Docker

Experience Required: 8+ years of overall IT experience

Strong hands‑on experience with AWS cloud services

Experience implementing MLOps pipelines and ML lifecycle management

Strong Python development skills

Hands‑on experience with SageMaker Pipelines, Model Registry, and Feature Store

Experience with CI/CD tools and cloud-native development practices

Education Requirements: Bachelor's degree in Computer Science, Engineering, Information Technology, or related field

AWS certifications preferred

📌 AWS Sagemaker / MLOPS Engineer (Toronto)
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