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IMCS Group

Machine Learning Engineer

ON, Canada
Ontario, Canada, Canada

Contract

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Job details

Job type: Contract
Apply by: Nov 8, 2026
Experience: 6+ years of overall IT experience
Posting language: English
Posted: Oct 9, 2026

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

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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

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

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