Especialista em engenharia de dados (Data Eng - Data Architecture)
BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- AWSDockerPythonKubeflowKubernetesMLFlowCI/CDTerraformComplianceCloudFormationMLOpsGenerative AI
Requirements
- Strong experience in MLOps, including automation, orchestration, and lifecycle management of machine learning models.
- Advanced knowledge of containerization and orchestration tools such as Docker and Kubernetes.
- Solid experience with cloud platforms, especially AWS, applied to data and machine learning workloads.
- Proficiency in Python for data engineering, automation, and ML operations tasks.
- Experience implementing monitoring, logging, and observability frameworks for production ML systems.
- Strong understanding of data governance, security, and compliance best practices.
- Experience working with CI/CD pipelines in data or machine learning environments.
- Familiarity with ML frameworks and MLOps tools such as MLflow, Kubeflow, or SageMaker Pipelines is highly desirable.
- Knowledge of infrastructure as code tools such as Terraform or CloudFormation is a plus.
- Experience optimizing performance of data pipelines or ML systems is considered an advantage.
- Exposure to generative AI projects or integration of advanced AI technologies is a plus.
- Strong collaboration, documentation, and communication skills in multidisciplinary teams.
- Active participation in technical communities, events, or knowledge sharing initiatives is valued.
Responsibilities
- Design, build, and maintain scalable MLOps pipelines supporting the full machine learning lifecycle, including training, deployment, and monitoring.
- Develop and optimize CI/CD pipelines tailored for machine learning workflows and data-driven applications.
- Implement monitoring, logging, and observability solutions to track model performance in production environments.
- Define and apply strategies to detect, diagnose, and mitigate model performance degradation over time.
- Ensure data security, governance, and compliance standards are maintained across production systems and ML workflows.
- Collaborate with cross-functional teams to document, standardize, and improve MLOps and data engineering processes.
- Support the integration of machine learning models into production systems using modern data architecture principles.
- Contribute to the evolution of scalable and automated data infrastructure supporting AI and analytics initiatives.
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