Tasks: Contribute to the development, testing, implementation, and integration of processes with business applications that utilize machine learning model predictions and classifications to inform or drive business activities. Leverage understanding of models and collaborate with Data Scientists to refactor the code into IT maintainable solutions that follows best practices and meets appropriate coding standards. Adhere to and applies ML development standards and coding best practices. Work with cross-functional team to contribute to machine learning projects throughout the machine learning lifecycle to include analysis, solution design, data pipeline engineering, testing, deployment, scheduling, production support, API development, and application integration in support of GenAI applications, ML frameworks/libraires, and ML models. Assist with designing and writing test scripts to verify data integrity and application of functionality. Review functionality of existing test scripts for understanding. Develop familiarity of machine learning engineering best practices by participating in trainings, reviewing documentation, and reading code from existing solutions. Configure, manage, and set up AI/ML infrastructure components in cloud/on-prem environments for projects and the AI/ML community stakeholders, including AWS, GCP, graph databases. Ensure risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures. May allow for partial telecommute.