Senior Machine Learning Engineer
New
J
JobgetherData Science
BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Intermediate English proficiency
- Required Skills
- PythonSQLMLFlowData engineeringCI/CDDatabricksMLOpsDistributed SystemsPySpark
Requirements
- Advanced experience with Databricks, including MLflow, Unity Catalog, Delta Lake, Databricks Workflows, and Model Serving.
- Strong experience developing, operationalizing, monitoring, and supporting Machine Learning models in production environments.
- Solid knowledge of Feature Engineering, hyperparameter optimization, and supervised/unsupervised learning algorithms.
- Practical experience with enterprise Feature Stores, feature versioning, and point-in-time lookups.
- Knowledge of Data Drift, Concept Drift, Performance Drift, and observability practices.
- Strong proficiency in Python, PySpark, SQL, MLflow, and Spark MLlib.
- Experience with distributed processing, Spark workload optimization, and designing scalable data workflows.
- Experience building CI/CD pipelines, managing multiple deployment environments, and implementing Infrastructure as Code.
- Experience implementing automated testing for data pipelines and Machine Learning workflows.
- Knowledge of secure credential and secrets management using Service Principals or Key Vault.
- Intermediate English proficiency for global team interaction and technical documentation.
- Databricks Certified Machine Learning Professional certification is highly desirable.
Responsibilities
- Lead MLOps initiatives covering model training, deployment, serving, monitoring, lifecycle management, and governance for production Machine Learning solutions.
- Develop and maintain ETL/ELT pipelines, DAGs, data workflows, and Machine Learning workflows using PySpark and distributed processing technologies.
- Design and manage enterprise Feature Stores, ensuring feature versioning, lineage, consistency between training and inference, and reliable point-in-time lookups.
- Develop, validate, deploy, and operationalize Machine Learning models across different analytical use cases.
- Implement model versioning, Champion/Challenger strategies, rollouts, model promotion processes, and Model Registry management.
- Ensure quality, traceability, reproducibility, auditability, and governance, including monitoring for data, concept, and performance drift.
- Design and implement CI/CD processes and Infrastructure as Code for Machine Learning platforms.
- Define architectural standards, MLOps guidelines, and automated testing approaches.
- Conduct code reviews, support Data Scientists in industrializing ML solutions, and contribute to architectural decisions.
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