Machine Learning Engineer | Senior
Accountabilities 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:
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.
Benefits:
Remote work opportunity within Brazil.
Opportunity to work on the industrialization and scaling of production machine learning models.
Hands-on exposure to AWS, SageMaker, PyTorch, MLOps, and modern AI engineering practices.
Opportunity to contribute to a Voice of the Customer platform that transforms multi-channel data into business insights.
Environment focused on Artificial Intelligence, Generative AI, advanced technologies, and digital transformation.
Access to AI-driven tools and cutting-edge technologies supporting the development of digital-native platforms.
Opportunities for continuous learning, knowledge sharing, and exposure to evolving technology trends.
Strong potential for personal and professional growth in a technology-driven environment.
Opportunity to collaborate with talented, multidisciplinary professionals on impactful AI initiatives.
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