Responsibilities
• Build and improve machine learning and analytics platform.
o Apply cutting edge technologies and tool chain in big data and machine learning to build machine learning and analytics platform.
o Keep innovating and optimizing the machine learning workflow, from data exploration, model experimentation/prototyping to production.
o Provide engineering solution and framework to support machine learning and data-driven business activities at large scale.
o Perform R&D on new technologies and solutions to improve accessibility, scalability, efficiency and us abilities of machine learning and analytics platform.
• Work with data scientists to build end-to-end machine learning and analytics solution to solve business challenges.
o Turn advanced machine learning models created by data scientists into end-to-end production grade system.
o Build analytics platform components to support data collection, exploratory, and integration from various sources being data API, RDBMS, or big data platform.
o Optimize efficiency of machine learning algorithm by applying state-of-the-art technologies, i.e. distributed computing, concurrent programming, or GPU parallel computing.
• Establish, apply and maintain best practices and principles of machine learning engineering.
o Study and evaluate the state of the art technologies, tools, and frameworks of machine learning engineering.
o Contribute in creation of blueprint and reference architecture for various machine learning use cases.
o Support the organization in transformation towards a data driven business culture.
Requirements
• PhD/Masters/Bachelors in Computer Science, Computer Engineering, Statistics, Applied Mathematics, or related disciplines.
• 5+ years of experience in software engineering or DevOps automation or data engineering
• Excellent understanding of software engineering principles and design patterns.
• Excellent programming skills in either Python or Java.
• Hands-on experience in containerization/ virtualization platforms, e.g. Docker/Kubernetes.
• Exposure to data science and machine learning technologies and methodologies.
• Good working knowledge of high performance computing, parallel data processing, and big data stack, e.g. Spark and Hadoop/Yarn.
• Experience to one or more commercial / open source data warehouses or data analytics systems, e.g. Teradata, is a big plus.
• Experience to one or more NoSQL databases is a big plus.
• Experience or Cloudera Data Science Workbench, is a big plus.
• Passion about machine learning and data-driven intelligence system.
• Excellent communication and presentation skills in English.
• Team player, self-starter, ability to work on multiple projects in parallel is necessary.
• Experience working in multi-cultural environments
