Machine Learning Engineer- AWS SageMaker
- Toronto, ON, Canada
Full-time
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Job details
- Job type
- Full-time
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- Posting language
- English
- Posted
Original job description
Machine Learning Engineer- AWS SageMaker
Toronto, ON
Hybrid: 2 days per week in-person at Toronto office preferred
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
More about this job
Responsibilities
Design and implement end-to-end machine learning pipelines, feature management strategies, and automated model deployment for enterprise-scale platforms. Operationalize machine learning solutions on AWS, including monitoring and CI/CD integration.
Requirements
Requires 8+ years of overall IT experience, strong AWS cloud and Python expertise, and hands-on experience with SageMaker, Feature Store, MLOps pipelines, and machine learning lifecycle management. A bachelor's degree in a related field is required; AWS certifications are preferred.
Skills
- AWS SageMaker
- SageMaker Feature Store
- MLOps
- Python
- Machine Learning
- Feature Engineering
- SageMaker Pipelines
- SageMaker Model Registry
- CI/CD
- AWS Bedrock
- Generative AI
- Terraform
- CloudFormation
- Kubernetes
- Docker
- Azure Machine Learning
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Data & Analytics
- Engineering
Keywords
- AWS SageMaker
- SageMaker Feature Store
- SageMaker Pipelines
- SageMaker Model Registry
- AWS Bedrock
- Generative AI
- Python
- Azure Machine Learning
- Terraform
- CloudFormation
- Kubernetes
- Docker
- MLOps
- Machine Learning
- Feature Engineering
- Feature Management
- Model Development
- Model Training
- Model Deployment
- Model Monitoring
- CI/CD
- Cloud-Native Development
- AWS Cloud Services
- Enterprise Machine Learning Platforms
- AWS Certifications