We are looking for a Machine Learning Engineer to join a Singaporean MNC’s Data Science team. As the group expands into using more advanced Data Science technologies, they are
looking for experts to come in and add value within a new area. 

You’d be working with domain experts and data scientists to work on advanced analytics problems, making sure that the Machine Learning algorithms created are deployed and working in the production environment.

Responsibilities

• Have strong experience running a full lifecycle of Machine Learning models – putting them into production. Being able to go from feature monitoring to testing and driving results for performance.
• Be able to design, develop and deploy these models into a production environment for the team.
• Schedule and orchestrate complex work-flows to ensure production happens on time.
• Be capable in optimizing the models to create efficient algorithms
• Work with a range of experts from Data Scientists to Engineers across the IT team, building a platform and framework that allows Machine Learning to be applied at a large-scale to business activities
• Create a continuous culture of innovation and workflow in Machine Learning by researching the newest technologies on the market

Requirements

• Minimally a Bachelors in Computer Science, Engineering, Mathematics, Statistics or other related fields
• Minimally 5 years total work experience, preferably where you’ve had a mix of research and machine learning roles as well as software or data engineering roles.
• Prior experience deploying machine learning models through a variety of tools, especially preferable if these have been on high-performance computing tools (e.g. Spark, Airflow, Kubernetes, Jenkins or Dockers)
• Proficiency programming on at least one of the following languages: Python, Java, C++
• Be fluent in Machine Learning frameworks (e.g. Tensorflow, Kaffe, PyTorch)
• Have excellent communication skills in English, being able to communicate with highly technical members as well as less technical teammates and translate complicated machine learning concepts to the layperson.

Reaching out