Engenheiro de Dados Sênior MLOps

Team: IT

Location: Brazil

Commitment: Full-time

Workplace Type: remote

Accountabilities:

The role is responsible for ensuring the successful deployment, operation, and continuous improvement of machine learning solutions, supporting scalable data architectures and efficient AI delivery processes. Key responsibilities include:

• Develop and maintain MLOps pipelines for model deployment, management, and operationalization.
• Build data preparation, transformation, and validation pipelines using PySpark and Databricks.
• Deploy, version, and manage the lifecycle of machine learning models in production environments.
• Automate model training, validation, deployment, and monitoring processes.
• Implement CI/CD practices for machine learning projects using GitHub and related tools.
• Monitor model performance, availability, data quality, and system stability.
• Ensure model traceability, governance, and reproducibility throughout the entire lifecycle.
• Integrate Azure, Databricks, and other components within modern data architectures.
• Collaborate with multidisciplinary teams to deliver scalable data and AI solutions.
• Identify opportunities to improve performance, optimize costs, and increase operational efficiency.

Requirements:

The ideal candidate should have strong technical expertise in data engineering, machine learning operations, and cloud-based platforms, combined with the ability to collaborate across technical and business teams. Required qualifications include:

• Solid experience deploying and operationalizing machine learning models in production environments.
• Advanced experience with Databricks.
• Strong knowledge of Azure cloud services.
• Advanced proficiency in PySpark for distributed data processing.
• Experience with GitHub and software version control practices.
• Knowledge of MLOps concepts, including automation, monitoring, governance, and model lifecycle management.
• Experience building and maintaining data and machine learning pipelines.
• Experience working with production environments and scalable architectures.
• Knowledge of CI/CD practices applied to machine learning projects.
• Experience with model monitoring, data quality management, and observability practices.
• Strong analytical thinking, problem-solving skills, and ability to work collaboratively with different teams.

Preferred qualifications include:

• Experience with MLflow, Azure Machine Learning, or similar tools.
• Knowledge of Lakehouse architecture concepts.
• Experience working in agile and technology-driven environments.

Benefits:

• Health and dental insurance.
• Meal and food allowance.
• Childcare assistance.
• Extended parental leave.
• Access to gyms and wellness professionals through Wellhub and TotalPass partnerships.
• Profit Sharing and Results Participation (PLR).
• Life insurance.
• Continuous learning platform with professional development resources.
• Discount club partnerships.
• Free online platform focused on physical, mental, and overall well-being.
• Pregnancy and responsible parenting courses.
• Partnerships with online learning platforms.
• Language learning opportunities.
• Additional benefits and employee support programs.