1. Deploy and integrate AI/machine learning models
- Develop and operate components within the MLOps platform.
- Partner with data scientists to build and operate Feature Store.
- Collaborate with relevant units to build/acquire new data sources for AI Center, EDA to facilitate developing new usecases.
- Support the validation of machine learning models.
- Design deployment architecture for machine learning model, leverage on premise and cloud based big data platforms to refactor and optimize code for production.
- Automate data and machine learning engineering processes.
- Monitor model quality post-deployment; support initiatives to improve model quality.
2. Conduct research and acquire new machine learning techniques
- Conduct research on modern methods for AI/machine learning and engineering.
- Proactively analyze and utilize existing/new data sources to support more impactful analyses.
3. Collaborate with business units on advanced analytics-related problems
- Working with other centers/departments in EDA as well as Business Units to understand business problems to support them in better utilizing machine learning.
- Support other centers/departments in providing prescriptive and predictive analyses when needed.
4. Training: Training other EDA team members on machine learning engineering.
