Software Development Engineer - QuickSight Q ML Pipeline Engineer @ Amazon Dev Center U.S., Inc. - Seattle, WA
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- 2+ years of non-internship professional software development experience
- Programming experience with at least one modern language such as Java, C++, or C# including object-oriented design
- 1+ years of experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems.
- Interest and experience in working with ML systems, particularly from an engineering standpoint
- Interest and experience with the Full Stack, both backend and frontend service development.
- Experience with data processing pipelines
Quicksight Q is looking for an engineer that enjoys working with Machine Learning pipelines. As an ML Pipeline Engineer, you will work in a very agile environment, employing your talents in both Full Stack engineering and Data Engineering to accelerate the data pipeline that trains the AWS QuickSight Q Natural Language Query engine.
Q is a machine learning-powered natural language capability that empowers business users to ask questions about all of their data using everyday business language and get answers in seconds. For example, users simply type “what is our year-over-year growth rate” and get an instant answer in QuickSight as a visualization.
Amazon QuickSight is revolutionizing Business Intelligence by empowering anyone to use the power of machine learning and Amazon AI to enhance their understanding of data. QuickSight can already help discover hidden insights using Anomaly Detection, enable ML-powered forecasting, and use automatic text generation generate business narratives directly from the data. But we're not stopping here, we remain with helping our customers harness the power of artificial intelligence to help them understand and visualize their data.
- 2+ years Python based backend such as Django or Flask. Flask is preferred.
- 2+ years experience with data processing tooling, such as Redshift/SnowFlake, Apache Spark, Pandas, Jupyter Notebooks, and SQL (Python preferred)
- Master's or Ph.D. in Computer Science, particularly with an emphasis on data science or related fields
- Experience architecting, designing, and putting full stack services into production
- Experience putting ML training data pipelines into production
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