AWS Sagemaker/MLOPS Engineer
Job Description & How to Apply Below
Hybrid: 2 days per week in-person at Toronto office preferred
Role
Description:
We are looking for a highly experienced Senior AWS Sage Maker / 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 Sage Maker, Sage Maker 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 Sage Maker (Model Development, Training, Deployment, Monitoring)
AWS Sagemaker / MLOPS Engineer 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 Sage Maker / 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 Sage Maker, Sage Maker 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 Sage Maker (Model Development, Training, Deployment, Monitoring)
AWS Sage Maker Feature Store and Feature Engineering
MLOps and Python Development
Top 3 Preferred
Skills:
AWS Bedrock / Generative AI
Terraform / Cloud Formation
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 Sage Maker 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
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