Role Bullets
Build data pipelines for analytics and machine learning (ML) purposes
Design build test and deploy ML models
Prepare data programmatically for statistical modeling
Deploy and manage instances of ML models/containers within the data pipeline
Document ML deployments and data pipelines
Job Description
The AI/ML Engineer will work with the data architect and data analytics team to construct data storage and data pipelining solutions for ML and analytic products. They will manage the creation of data storage solutions for data retrieved from TCTP as well as data quality and transformation pipelines for any data intended for insight generation (through ML services or statistical modeling). AI/ML Engineers will configure required cloud services and containers for data analytics or ML modeling work and will have responsibility for deploying productionized ML models and ensuring that all outputs are available for dashboarding. They will also contribute to the documentation of the ML deployments and data pipelines for which they are responsible
Qualifications
3 years of experience with SQL
Experience with Python for statistical or ML purposes
Experience and understanding of feature engineering for ML purposes
Experience with Spark/PySpark AWS Glue or other ETL tools
Familiarity with any requisite data architecture or technologies used by the client (AWS)
Experience with Sagemaker and other AWS ML tools
Experience implementing and managing instances of ML models/containers within the client data tech stack
Common Optional Qualifications
Can help with data normalization quality assurance data manipulation etc. for preparation for ML instances
Ability to communicate effectively with system data engineers and clients to understand how best to architect
sagemaker,etl tools,artificial intelligence,ml,aws glue,aws ml tools,python,aws,feature engineering,data manipulation,quality assurance,sql,python for data analysis,data normalization,spark/pyspark,spark