AIML Engineer Energy Client

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