About the Company:

Ouro is dedicated to delivering financial empowerment to millions of Americans, leveraging a proprietary payments technology platform that fuels its fintech product innovations. From prepaid, credit and debit account solutions, to digital account and money movement services, Ouro has a broad suite of products and technologies that deliver exceptional experiences for its customers and business partners across co-branded, white label and banking-as-a service verticals. Since its founding in 1999, Ouro products have helped millions of consumers nationwide, and processed tens of billions of dollars.

Present day: Legendary Austin fintech Ouro has opened a new chapter in its role as market maker with the return of its visionary founders Roy and Bertrand Sosa. The combined company meshes Ouro’s customer and partner portfolios, payments platform and innovations with the international footprint and money movement capabilities of Rêv Worldwide, the company the Sosas founded after Ouro. The reunion creates a revitalized financial services innovator on a mission to reimagine financial services for consumers around the world and to redefine the industry once again.

Ouro fosters a high-performance culture and we are building a unified platform and product suite capable of bringing financial mobility and freedom to consumers around the world despite differences in language, currency, culture, and geography.

Senior Machine Learning Engineer:

As a Machine Learning Engineer (MLE) on an MLOps project, you will be responsible for transforming the initial prototypes created by the Data Scientists (DS) during the discovery phase into production-ready solutions. This involves working closely with DS who use Jupyter notebooks and the Feature Store to develop the initial data as well as training, serving, and feedback workflows, ensuring they are scalable, efficient, and reliable. Additionally, you will be responsible for evolving the different workflows, and continuously improving and adapting them to meet new requirements and challenges.

Roles & Responsibilities :

Creating & Maintaining feature definitions (shared with DS) - Productionalize the data scientist's notebook code & create AIO Products(Workflows/Processes/Config) for training/serving the model, updating feature values, and evaluating model performance - Maintaining docker images (Define Docker files) for each process. - Evaluating and Monitoring the model - Monitoring full solution (workflows), debugging and troubleshooting production issues related to model and pipelines

Skills : 

Programming Languages:

Knowledge of Python

(Optional but recommended) Knowledge of Golang

Tools:

Git

Docker

Grafana

Redis

(Optional but recommended) Kubernetes

Feast or other similar Feature Stores

Experience:

Minimum of 7 years of experience in a Machine Learning Engineer role

Building data pipelines

Working with the control version and CI/CD

Experience with deploying Machine Learning models

Troubleshoot and debug production issues related to pipelines and models

Asynchronous and event-oriented programming