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Pacer Group

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: Nov 7, 2026
Posting language: English
Posted: Oct 8, 2026

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.

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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

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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

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