We are looking for Machine Learning Engineers to join our Data team. The incumbent will closely collaborate with the Data Science team in feeding data (across functions such as, credit scoring, fraud, and risk) into ﬁnancial models deﬁned by data scientists. This role is also responsible to make sure that data science code is maintainable, scalable and debuggable.
- Develop and implement ML pipeline infrastructure, including but not limited to: ETL (Extract, Transform and Load) of raw data into model features and building platform to automate ML training, testing and maintenance.
- Partnering with Data Scientists to transform prototypes of predictive models into high performance, well integrated systems.
- 3-5 years of relevant experience in Data or Software Engineering and minimum 1-2 years hands-on experience in deploying machine learning models and feature pipelines
- Having good knowledge of data structures, data modeling and software architecture related to Machine Learning
- Ability to write robust production-ready code in Python
- High degree of proﬁciency with RDBMS (i.e. MySQL, PostgreSQL) and No-SQL platforms (i.e. S3)
- Proﬁciency in machine learning libraries such as: Tensorﬂow/Keras, Pytorch, SparkML or MLeap
- Strong interest in building scalable and reliable Machine Learning Engineering/AI related services
- Past experience working with cloud services platform to build data pipelines, monitoring, scheduler and storage, preferably on AWS environment.
- Familiarity using a distributed computing platform, such as Hadoop or Spark.