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 the 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 for ensuring that data science code is maintainable, scalable, and debuggable.

Job Responsibilities

1. Develop and implement ML pipeline infrastructure, including but not limited to: ETL (Extract, Transform, and Load) of raw data into model features and building a platform to automate ML training, testing, and maintenance.
2. Partner with Data Scientists to transform prototypes of predictive models into high-performance, well-integrated systems.

Qualifications

1. 2-3 years of relevant experience in Data or Software Engineering and a minimum of 1-2 years hands-on experience in deploying machine learning models and feature pipelines.
2. Good knowledge of data structures, data modeling, and software architecture related to Machine Learning.
3. Ability to write robust production-ready code in Python & GoLang.
4. High degree of proficiency with RDBMS (i.e., MySQL, PostgreSQL) and No-SQL platforms (i.e., S3).
5. Proficiency in machine learning libraries such as Tensorflow/Keras, Pytorch, SparkML, or MLeap.
6. Strong interest in building scalable and reliable Machine Learning Engineering/AI-related services.
7. Past experience working with cloud services platforms to build data pipelines, monitoring, scheduler, and storage, preferably in an AWS environment.
8. Familiarity with 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.