Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A leading digital bank in Latin America serves more than 135 million customers across Brazil, Mexico, and Colombia. The organization is transforming the financial services industry through data and proprietary technology, developing innovative products and services that promote financial access, transparency, responsible lending, and greater financial empowerment.
Key Responsibilities
- Design and build scalable data ingestion pipelines that bring new datasets into the AI Core platform.
- Ensure reliable and efficient data flow from source systems through model training.
- Implement data quality monitoring and validation systems to identify issues before they affect model performance.
- Maintain the health, reliability, and integrity of datasets across the machine learning ecosystem.
- Model new types of data for integration into foundation models.
- Analyze the impact of new data sources on existing models through experiments and performance evaluations.
- Develop and maintain data preparation workflows that transform raw data into features suitable for model training.
- Work with distributed computing frameworks such as Ray, Spark, or similar technologies.
- Tune and optimize machine learning models when new datasets are integrated.
- Apply hyperparameter optimization techniques and evaluate model performance improvements.
- Collaborate with AI Core ML, Platform, and Infrastructure teams to maintain seamless data flow throughout the machine learning infrastructure.
- Lead technical initiatives that improve data engineering practices, pipeline reliability, data quality, and model-data integration.
- Establish technical standards and best practices for scalable ML data systems.
- Mentor team members and contribute to hiring initiatives.
- Help build a strong and diverse engineering team focused on innovation in AI infrastructure.
Required Qualifications
- Typically 6+ years of experience in machine learning engineering, data engineering, or related fields.
- Strong track record of building production-grade data and machine learning systems.
- Proven experience designing and building large-scale data ingestion pipelines.
- Experience with distributed computing frameworks such as Ray, Spark, or similar technologies.
- Strong background in applied machine learning, including model training, hyperparameter tuning, and performance evaluation.
- Experience analyzing how changes in datasets affect model performance.
- Ability to design and execute experiments that measure model and data improvements.
- Proficiency in Python for data engineering and machine learning workflows.
- Experience working with large-scale data processing systems.
- Strong understanding of data quality principles.
- Experience implementing data monitoring, validation, and alerting systems.
- Strong problem-solving skills and the ability to address complex and ambiguous technical challenges.
- Ability to coordinate effectively across multiple teams.
- Excellent communication skills with the ability to explain technical concepts to technical and non-technical stakeholders.
- Demonstrated leadership experience, including mentoring team members and contributing to technical decision-making.
Preferred Qualifications
- Experience with MLflow or similar machine learning model tracking and versioning platforms.
- Knowledge of foundation models, fine-tuning workflows, and transformer architectures.
- Experience with data pipeline orchestration tools such as Dagster, Airflow, or similar platforms.
- Background in financial services or fintech.
- Understanding of the unique data and technology challenges associated with financial services.
- Experience working in fast-paced, high-growth environments with distributed teams.
- Demonstrated ability to reduce complexity in data systems.
- Experience improving developer experience and productivity for machine learning teams.
Disclaimer
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- No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
- No Client Relationship: This company is not a client of SpringCube unless stated.
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