ML Engineer Data and ML Training Pipeline Engineer

Experience Level: 3-7 Years

(ML Engineer Data and ML Training Pipeline Engineer)

• Proficient in Python language. Familiar with Tensorflow/Keras frameworks
• Experience in building ML Pipeline and setup big data processing pipelines
• Able to evaluate model and benchmark various model version on different data and report generation
• Experience in Vision Domain ML models would be an advantage but not must have
• Comfortable in generating data as per model format, tfrecords, lmdb or similar format.
• Comfortable in data analysis and visualization
• Knack for debugging of python compilation, data reading issues.
• Proficiency in usage of different development tools – git/gerrit, static/dynamic analysis tools, code coverage, test & performance analysis tools

Good to have:

• Comfortable with building data analysis, annotation scripts and exploring tools for ease of data understanding and annotations.
• Knack for data debugging and verify which classes/types of data are causing the ML model to fail. Should be comfortable in writing scripts to filter out/analyze such images and should be able to draw a conclusion after verifying the failure cases
• Experience in data visualization and cleaning for AI/ML projects.
• Synthetic Data Generation experience
• Able to track and version data used in ML pipeline and report generation