Our Purpose
We work to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart and accessible.
Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments and businesses realize their greatest potential.
Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company.We believe that our differences enable us to be a better team - one that makes better decisions, drives innovation and delivers better business results.
Title and SummaryOur mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making payment and data transactions safe, simple, smart, and accessible.
Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential.
OverviewThe team is responsible for leading the implementation of AI/ML based solutions, proposing the right architecture & technologies, and evaluating the evolution of the architecture as the needs change.
For this team, Mastercard is seeking a Principal Software Engineer who is passionate about implementation of AI/ML assets across platform (on premise, on cloud, hybrid).
The person would be working closely with Product, Program as well Data Science teams.Responsible for accelerating modern architecture-based development or deployment of AI/Machine Learning solutions using lightweight stack and scaled version of modelling techniques.
Provide service to other engineering teams across organization, cross functions to deliver quality architecture for AI/ML model deployments or serving.
ExperiencesExperience in building and deploying AI/ML models in enterprise production environments/large scale projects with modern lightweight design (API, Microservices etc.).
Hands-on experience in standing up K8 S based AI/ML platform as well as working with workloads inside Kubernetes environment is required.
Good knowledge of Machine learning - bias-variance trade off, exploration/exploitation - and understanding of various model families, including neural net, decision trees, Bayesian models, deep learning algorithms.
Experience with ML frameworks and libraries like Tensor Flow, Keras, Py Torch, Kubeflow etc.