Senior Machine Learning Engineer - Hà Nội - TA150

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.

• Bachelor’s degree in mathematics, Statistics, Engineering, Computer Science or other Quantitative discipline
• Minimum 3-5 years of solid experience in data science, machine learning or big data engineering.
• Proven experience in deploying AI/machine learning model to production.
• Have knowledge and ability to work with cloud platforms.
• Experience in data exploration/interpretation, working with statistical models, forecasting, machine learning algorithms, advanced analytical techniques.
• Familiar with SQL and experience with data transformation.
• Strong proficiency in at least one programming language Python/R.
• Deep understanding of AI/machine learning, big data technologies (including Hadoop/Spark), distributed computing, software development and visualization.