Senior Machine Learning Engineer
Design and implement ML models and data ingestion pipelines in production environment using the preprocessing, training scripts provided by data scientists following enterprise coding standards. Develop and support a platform that enables data scientists to rapidly develop, train, and experiment with machine learning models and help them utilize features provided by platform including data science lab, cross account access, access to data produced and captured at model endpoints.
Identify and implement internal process improvements including automating manual processes, optimizing data delivery, and redesigning infrastructure for greater scalability. Implement Machine Learning (ML) and Big Data platforms in Hybrid and multi- cloud environment specifically in AWS SageMaker environment.Lead and drive machine learning projects from inception to production: build relationships with business partners and cross-functional teams including DevOps, cloud engineering, AI/ML platform team and business users. Design, develop & deliver critical components, frameworks, services, and products using AWS SageMaker, Lambda, and container technologies in AWS.
Provide support to models deployed in production through releasing hotfixes or enhancements as needed. Provide support for retraining and refreshing the models by data scientists that are currently being used in production.
Telecommuting permitted 100%. Candidate may reside and work anywhere in the U.S.
Full time employment, Monday Friday, 40 hours per week.
MINIMUM REQUIREMENTS:
Must have a Bachelors degree in Applied Computer Science, Information Technology, or a related field and 3 years of related work experience.
Of the required experience, must have 3 years of experience in each of the following:
Deploying machine learning models to production, supporting building of machine learning system, data pipelines and infrastructure required for optimal feature engineering, data extraction, data transformations, and loading of data from a wide variety of data sources;
Working with data scientists, enterprise architecture team, data engineering design teams, ML platform team to assist with machine learning related technical issues and implement ML models in AWS Sagemaker;
Utilizing Numpy, Pandas, Scikit-Learn, Git, JIRA, Jupyter Notebook, IAM, EC2 and S3;Utilizing computer language tools including: C++ and Python; and
ML algorithms: XGBoost, LR and Deep Learning (CNN andRNN).
Of the required experience, must have 2 years of experience in utilizing Tensorflow.
Telecommuting permitted 100%. Candidate may reside and work anywhere in the U.S.