AWS Sagemaker
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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 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 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
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