Machine Learning Engineer- AWS SageMaker

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

Machine Learning Engineer- AWS SageMaker Toronto, ON Hybrid: 2 days per week in-person at Toronto office preferred Skills: Digital: Python | Digital: Azure Machine Learning (ML) | Digital: Terraform | AI & Gen AI - Products & Tools Experience Required: 6–8 Years 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 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 8+ years of overall IT experience, strong AWS cloud and Python expertise, and hands-on experience with SageMaker, Feature Store, MLOps pipelines, and machine learning lifecycle management. A bachelor's degree in a related field is required; AWS certifications are preferred.

Listed skills

• Python · 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
• SageMaker Feature Store
• MLOps
• Python
• Machine Learning
• Feature Engineering
• SageMaker Pipelines
• SageMaker Model Registry
• CI/CD
• AWS Bedrock
• Generative AI
• Terraform
• CloudFormation
• Kubernetes
• Docker
• Azure Machine Learning

Job areas

• Technology
• Software
• Data & Analytics
• Engineering

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