AWS Sagemaker/MLOPS Engineer
Listed on 2026-10-13
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Software Development
AWS, Machine Learning/ ML Engineer, AI Engineer (Applied/Software)
Hybrid: 2 days per week in-person at Toronto office preferred
RoleDescription:
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 3Required Skills:
- AWS Sage Maker (Model Development, Training, Deployment, Monitoring)
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
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 3Required Skills:
- AWS Sage Maker (Model Development, Training, Deployment, Monitoring)
- AWS Sage Maker Feature Store and Feature Engineering
- MLOps and Python Development
Skills:
- AWS Bedrock / Generative AI
- Terraform / Cloud Formation
- Kubernetes and Docker
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
- Bachelor's degree in Computer Science, Engineering, Information Technology, or related field
- AWS certifications preferred
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