4h ago

Machine Learning Engineer | Senior

BrazilRemoteFull-timesenior

Machine Learning EngineerData

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Quick Summary

Requirements Summary

Proven professional experience developing and deploying machine learning solutions using Python in production environments.

Technical Tools

Machine Learning EngineerData

This role offers the opportunity to industrialize and scale the core machine learning model behind a Voice of the Customer (VoC) platform. You will transform experimental models developed in notebooks into robust, production-ready pipelines running on AWS. The position combines hands-on machine learning engineering with MLOps, model governance, statistical validation, and platform evolution. You will work with technologies such as Python, PyTorch, and Amazon SageMaker to build scalable training and inference workflows. Your work will directly contribute to turning data from multiple customer channels into actionable business insights. This is a senior-level opportunity in an AI-driven environment focused on engineering excellence, scalability, and continuous innovation.

Industrialize the core machine learning model of a Voice of the Customer platform, moving models from experimentation into reliable production environments.
Transform notebook-based machine learning solutions into scalable, parametrized, and production-ready pipelines on AWS.
Structure and maintain the MLOps lifecycle, ensuring robust model versioning, reproducibility, governance, and experiment management.
Develop and manage training and inference pipelines using Amazon SageMaker, including Jobs, Pipelines, Model Registry, and GPU-based workloads.
Perform statistical validation to ensure score parity and consistency between existing models and their production-migrated versions.
Contribute to the evolution of a VoC platform that integrates data from multiple channels to generate meaningful business insights.
Collaborate across technical and analytical initiatives to improve the reliability, scalability, and operational maturity of machine learning solutions.

Requirements

~1 min read

Proven professional experience developing and deploying machine learning solutions using Python in production environments.
Strong experience with PyTorch and hands-on development of machine learning models.
Practical experience with AWS SageMaker, including Jobs, Pipelines, Model Registry, and GPU-based workloads.
Solid understanding of MLOps practices, including model and experiment versioning, reproducibility, deployment, and governance.
Experience with statistical validation and model comparison, with the ability to assess model consistency and performance rigorously.
Strong analytical and problem-solving skills, with a hands-on approach to investigating technical challenges and improving ML systems.
Experience with PyTorch Geometric and Graph Neural Networks (GNNs) is a plus.
Knowledge of NetworkX, scikit-learn, and FAISS is considered an advantage.
Experience with machine learning model explainability is a plus.
Ability to work collaboratively in a technically sophisticated, AI-focused environment while maintaining strong ownership of deliverables.