Brazil onlyWe are assisting a leading cloud consulting firm specializing in cloud-native development, data and AI modernization, and secure cloud operations. As an AWS Premier Partner, they help organizations scale with cutting-edge technologies while fostering a culture of innovation, collaboration, and continuous learning.
As a Machine Learning Engineer, you’ll design, implement, and optimize end-to-end ML pipelines using AWS SageMaker, MLflow, and GitLab CI/CD. You'll work closely with data scientists and engineers to make sure models are trained, shipped, and monitored with performance, governance, and reliability in mind.
What you’ll be doing
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
Must-Have
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
Nice-to-Have
Experience with Snowflake, AWS Athena, and AWS Glue Data Quality
Familiarity with advanced MLOps governance workflows like approval gates and retraining triggers
Experience working in high-compliance or audit-ready environments
