Machine Learning Engineer

At Kredivo we are building the future of financial services that are fast, cheap and widely accessible, wrapped up in a beautiful UX. As we are growing bigger, we are looking for Machine Learning Engineer who will closely collaborate with Data Science team in feeding data (across functions such as, credit scoring, fraud, and risk) into financial models defined by data scientists. You will also be responsible to make sure that data science code is maintainable, scalable and debuggable.

Job Responsibility

• 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.

Qualifications

• 2-3 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 & GoLang
• 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

Thank you for your interest in Kredivo. We’re thrilled that you’d like to join our company. Unfortunately, days are short and applicants are many, so we won’t have time to meet everyone. If you are among qualified candidates, you will receive a call or email from our Talent Acquisition team.