Aws Sagemaker / Mlops Engineer

Hybrid:
2 days per week in-person at Toronto office preferredRole 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, ONHybrid:
2 days per week in-person at Toronto office preferredSkills:
Digital: Python | Digital: Azure Machine Learning (ML) | Digital: Terraform | AI & Gen AI - Products & ToolsExperience Required:
6–8 YearsRole 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 Feature Store and Feature EngineeringMLOps and Python DevelopmentTop 3 Preferred Skills:
AWS Bedrock / Generative AITerraform / CloudFormationKubernetes and DockerExperience Required:
8+ years of overall IT experienceStrong hands‑on experience with AWS cloud servicesExperience implementing MLOps pipelines and ML lifecycle managementStrong Python development skillsHands‑on experience with SageMaker Pipelines, Model Registry, and Feature StoreExperience with CI/CD tools and cloud-native development practicesEducation Requirements:
Bachelor's degree in Computer Science, Engineering, Information Technology, or related fieldAWS certifications preferred
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