Description
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 cultivate a culture of inclusion for all employees that respects their individual strengths, views, and experiences. 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 Summary
Principal Machine Learning Engineer Mastercard is a global technology company. Our 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.Overview
ML Engineering team leads AI/ML deployments across Mastercard platforms. The 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.
Responsibilities -
• Responsible for presenting AI/ML architecture design/details to Data Science, Program, Product and Architect group.
• Responsible to advance, improve, stand-up AI/ML framework over K8S based platform with architect and engineering groups.
• Responsible for accelerating modern architecture-based development or deployment of AI/Machine Learning solutions using light weight 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.
Experiences
• 8-10-year experience working in AI/ML technology domain or similar.
• Experience in building and deploying AI/ML models in enterprise production environments/large scale projects with modern light weight design (API, Microservices etc.)
• Hands on experience in standing up K8S 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 TensorFlow, Keras, Pytoch, Kubeflow etc.
• Prior experience with Enterprise AI/ML Architecture pillars– BDAT
• Ability to learn new technologies quickly and mentor other team members in AI/ML domain.
• Proven track record of delivering and willingness to roll up sleeves to get the job done.
• Current with industry trends on On-premise or Cloud native deployments.
• Proficiency with cloud technologies (IaaS, PaaS, serverless technology) micro-service design, CI/CD, DevOps.
• Excellent communication/presentation skills
Corporate Security Responsibility
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:
Abide by Mastercard's security policies and practices;
Ensure the confidentiality and integrity of the information being accessed;
Report any suspected information security violation or breach, and
Complete all periodic mandatory security trainings in accordance with Mastercard's guidelines.
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