Azure Data Engineer for AI and Digital Innovations
23 hours ago
We are seeking a skilled Mid-level Azure Data Engineer to join our AI & Digital Innovations teams at RIT Solutions, Inc.
Job Summary:
As a Mid-level Azure Data Engineer, you will be responsible for designing, developing, and implementing high-performance data solutions using Azure Data Factory, Azure Databricks, and Fabric for data integration and real-time analytics.
Key Responsibilities:
- Design, develop, and implement high-performance Python coding with a focus on efficiency, scalability, and performance
- Create robust and reusable code using Python that can independently generate creative content
- Collaborate closely with data scientists, machine learning engineers, and other stakeholders to understand their requirements and translate these into data-driven solutions
- Troubleshoot, debug, and resolve any issues that occur within the generative AI system development
- Conduct tests and experiments to enhance the performance of overall system and document the results for further improvements
- Document all processes, specifications, and training procedures, and maintain stringent version control to ensure the high quality, accuracy, and replicability of generative models
Requirements:
- Hands on experience with Azure Data Factory, Azure Databricks, and Fabric for data integration and real-time analytics
- Expert level on using standard Client libraries such as Numpy and Pandas
- Advanced level on using Client frameworks PyTorch/TensorFlow on Azure cloud ecosystem
- Background on Unit, Integration, and System testing
Preferred Qualifications:
- Azure certification would be a plus
Work Environment:
Fully remote work environment with estimated hours of support. USC, GC, or GC EAD only. Video interview process.
Company Overview:
RIT Solutions, Inc. is a leading provider of AI and Digital Innovations solutions. Our team brings the latest GenAI techniques plus a deep understanding of industry dynamics and corporate functions to help clients create the most value from data.
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