AI - Data Engineer

Hiring for Trainer profile for Data Engineer


Data Architecture and Design: Develop and maintain data architectures including data models data warehouses and data lakes to support AI and machine learning initiatives.

Data Pipeline Development: Design and implement data pipelines to extract transform and load (ETL) data from various sources into a centralized system where it can be used for AI applications.

Data Integration: Integrate data from multiple sources including databases APIs and external data feeds ensuring that data is consistent accurate and accessible.

Data Quality Management: Monitor and ensure the quality of data implementing validation checks and cleaning processes to maintain high standards of data integrity.

Performance Optimization: Optimize data processing workflows and queries to improve the performance of data pipelines and support efficient AI model training and inference.

Collaboration with Data Scientists: Work closely with data scientists and machine learning engineers to understand data requirements facilitate data access and provide support for the development and deployment of AI models.

Data Security and Privacy: Implement data security measures and ensure compliance with privacy regulations to protect sensitive information and maintain data confidentiality.

Scalability and Maintenance: Ensure data infrastructure is scalable to handle growing data volumes and perform routine maintenance to keep systems running smoothly.

Documentation and Reporting: Document data engineering processes architectures and pipelines and generate reports to communicate data insights and system performance to stakeholders.

Technology Evaluation and Adoption: Stay uptodate with emerging technologies and tools in the data engineering and AI space evaluating their potential benefits and integrating them into existing systems as appropriate.

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