AWS SageMaker Engineer
- Toronto, ON, Canada
$50 to $53 CAD / hour · Contract
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Job details
- Pay
- $50 to $53 CAD / hour
- Job type
- Contract
- Apply by
- Posting language
- English
- Posted
Original job description
Job Title: Senior AWS SageMaker / MLOps Engineer
Location: Toronto, ON
Work Arrangement: Hybrid-2 days/ week
Employment Type: Contract
Pay Rate: CAD 50-53/Hour
Domain: BFSI
Application Deadline: Oct. 30th 2026
Skills:
- 7+ years of overall IT experience
- Should have 2-3 years of experience on AWS SageMaker (Model Development, Training, Deployment, Monitoring), AWS SageMaker Feature Store and Feature Engineering.
- 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
- Nice to have exp. with AWS Bedrock / Generative AI/ Terraform / CloudFormation/ Kubernetes/ Docker
EEOC Compliance:
We are an equal opportunity employer, and all qualified applicants will receive consideration for employment.
DISCLAIMER
AI Usage Policy: Pacer Group uses AI to assist in screening applications. Final hiring decisions are made by human recruiters based on qualifications and experience.
More about this job
Responsibilities
Develop, train, deploy, and monitor machine learning models using AWS SageMaker and manage features with SageMaker Feature Store. Implement MLOps pipelines and machine learning lifecycle management using SageMaker Pipelines, Model Registry, and cloud-native CI/CD practices.
Requirements
Requires 7+ years of overall IT experience, including 2–3 years working with AWS SageMaker, and strong hands-on AWS cloud and Python development experience. Candidates should have experience with MLOps pipelines, machine learning lifecycle management, SageMaker Pipelines, Model Registry, Feature Store, and CI/CD; AWS Bedrock, generative AI, Terraform, CloudFormation, Kubernetes, and Docker are nice to have.
Skills
- AWS SageMaker
- MLOps
- AWS Cloud Services
- Python
- Model Development
- Model Training
- Model Deployment
- Model Monitoring
- SageMaker Feature Store
- Feature Engineering
- SageMaker Pipelines
- Model Registry
- CI/CD
- Cloud-Native Development
- AWS Bedrock
- Generative AI
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Data & Analytics
- Finance & Accounting
Keywords
- AWS SageMaker
- MLOps
- AWS Cloud
- Python
- Machine Learning
- Model Development
- Model Training
- Model Deployment
- Model Monitoring
- SageMaker Feature Store
- Feature Engineering
- SageMaker Pipelines
- Model Registry
- CI/CD
- Cloud-Native Development
- AWS Bedrock
- Generative AI
- Terraform
- CloudFormation
- Kubernetes
- Docker
- BFSI