Machine Learning Engineer

Job summary

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.

Job details

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

What you’ll do

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.

Listed skills

• Python · Preferred
• Amazon Web Services · Preferred
• Machine learning · Preferred
• CI/CD · Preferred
• Terraform · Preferred
• Kubernetes · Preferred
• Docker · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• 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

Job areas

• Technology
• Software
• Data & Analytics
• Engineering

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