Machine Learning Engineer
Siteup
full-time
Posted on:
Location Type: Remote
Location: Brazil
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About the role
- Build and maintain training pipelines using the AWS SageMaker SDK, with MLflow for experiment tracking
- Own the full model lifecycle: tracking, packaging, versioning, and registry management
- Implement and monitor real-time (SageMaker Endpoints) and batch (Batch Transform) inference pipelines
- Integrate model predictions with DynamoDB to support third-party enrichment and real-time workflows
- Set up monitoring for data drift, bias detection, and overall model health using SageMaker Model Monitor
- Maintain the MLflow Model Registry to ensure versioned, production-approved models
- Collaborate with the DevOps/infrastructure team to manage CI/CD/CT pipelines using GitLab, Terraform, and Terragrunt
Requirements
- Strong hands-on experience with AWS SageMaker, including Studio and Feature Store
- Proficiency with MLflow and solid understanding of artifact tracking and model versioning
- Fluent in Python, with experience building modular and scalable ML training pipelines
- Familiar with GitLab CI/CD, Terraform, and Terragrunt
- Strong grasp of model monitoring, including data capture, bias/drift detection, and production metrics
Benefits
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Hard Skills & Tools
AWS SageMakerMLflowPythonCI/CDmodel monitoringdata drift detectionbias detectionbatch inferencereal-time inferencemodular ML training pipelines