Machine Learning Engineer
- ON, Canada
- Ontario, Canada, Canada
Contract
Applications open in a new tab.
Job details
- Job type
- Contract
- Apply by
- Experience
- 6+ years of overall IT experience
- Posting language
- English
- Posted
Original job description
Senior AWS SageMaker / MLOps Engineer
Location: Toronto, ON
Work Model: 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 6+ 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 Required Skills
- AWS SageMaker – Model Development, Training, Deployment, and Monitoring
- AWS SageMaker Feature Store and Feature Engineering
- MLOps and Python Development
Top Preferred Skills
- AWS Bedrock / Generative AI
- Terraform / CloudFormation
- Kubernetes and Docker
Required Experience
- 6+ 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
Skills
- Python
- AWS SageMaker
- AWS SageMaker Feature Store
- MLOps
- AWS
- AWS Bedrock / Generative AI
- Terraform
- CloudFormation
- Kubernetes
- Docker
- CI/CD
- Azure Machine Learning (ML)
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 6+ years of overall IT experience, strong AWS cloud and Python development skills, and hands-on experience with SageMaker, Feature Store, and MLOps pipelines. Experience with SageMaker Pipelines, Model Registry, CI/CD tools, and cloud-native development practices is required; a relevant bachelor's degree is specified, and AWS certifications are preferred.
Skills
- AWS SageMaker
- AWS SageMaker Feature Store
- MLOps
- Python
- AWS
- Machine Learning
- Feature Engineering
- SageMaker Pipelines
- SageMaker Model Registry
- CI/CD
- AWS Bedrock
- Generative AI
- Terraform
- CloudFormation
- Kubernetes
- Docker
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Data & Analytics
- Engineering
Keywords
- AWS SageMaker
- SageMaker Feature Store
- MLOps
- Python
- AWS
- Machine Learning
- Feature Engineering
- SageMaker Pipelines
- SageMaker Model Registry
- CI/CD
- AWS Bedrock
- Generative AI
- Terraform
- CloudFormation
- Kubernetes
- Docker
- Azure Machine Learning
- Model Development
- Model Training
- Model Deployment
- Model Monitoring
- ML Lifecycle Management
- Feature Management
- Cloud-Native Development
- AWS Certifications
- Enterprise Machine Learning Platforms