Closed listing
Senior AWS SageMaker / MLOps Engineer
- Toronto, ON, Canada
Contract
This opening has closed.
The original listing is kept below for reference.
Job details
- Job type
- Contract
- Closing date
- Experience
- 8+ years of overall IT experience
- Posting language
- English
- Posted
Similar jobs hiring now
Search current openingsOriginal job description
Archived listing. The details below describe a past opening.
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
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 on AWS. Operationalize machine learning solutions through monitoring and CI/CD integration.
Requirements
Requires 8+ years of overall IT experience, strong AWS cloud and Python development skills, and hands-on experience implementing MLOps pipelines and managing the machine learning lifecycle. Candidates should have experience with SageMaker Pipelines, Model Registry, Feature Store, CI/CD tools, and cloud-native development practices.
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
- Model Monitoring
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Data & Analytics
- Engineering
Keywords
- AWS
- AWS SageMaker
- SageMaker Feature Store
- SageMaker Pipelines
- SageMaker Model Registry
- MLOps
- Machine Learning
- Python
- Feature Engineering
- Feature Management
- Model Development
- Model Training
- Model Deployment
- Model Monitoring
- End-to-End ML Pipelines
- CI/CD
- AWS Bedrock
- Generative AI
- Terraform
- CloudFormation
- Kubernetes
- Docker
- AWS Cloud Services
- Cloud-Native Development