Lead AI/ML Engineer

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

Lead AI/ML Engineer

Lead AI/ML 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 planning the implementation of solutions, choosing the right technologies, and evaluating the evolution of the architecture as the needs change.
For this team, MasterCard is seeking a Lead AI/ML Engineer who is passionate about implementation of AI/ML assets across platform (on premise, on cloud, hybrid). The person would be working closely with Program Team as well as Data Science team.
Responsibilities
• Responsible for deploying modern and large scale end-to-end AI/ML solutions
• Design and implementation of Kubernetes operators and micro-services for orchestrating AI/ML workloads
• Work cross-functionally with data scientists, data engineer, and business stakeholders to design, develop, deploy, and integrate high-performance machine learning pipelines and data intensive workloads
• Take ownership of production systems with a focus on delivery, continuous integration, and automation of machine learning workloads
• Provide technical mentorship, guidance, and quality-focused code review to data scientists and ML engineers

Experiences
• 5+ years of 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
• Experience in APIs and micro-service architectures
• Fundamental knowledge and hands-on experience with Kubernetes
• Hands-on experience with DataOps and MLOps - namely development, testing, deployment and monitoring of data and ML pipelines; using tools like MLFlow, KubeFlow, AirFlow or similar
• Experience with SQL and relational database systems like Oracle, PostgreSQL or MySQL
• Experience building large scalable and reliable enterprise technology platforms using Big Data open-source technologies such as Hadoop, Spark and Kafka
• Good knowledge and understanding of AI/ML model families, including neural net, decision trees, Bayesian models, deep learning algorithms(LSTM, CNN etc.)
• Proficiency with Python programming and machine learning libraries such as NumPy, Pandas, Scikit-learn, Tensorflow and/or PyTorch
• Ability to learn new technologies quickly and mentor Data Science team members in AI/ML domain
• Experience with Continuous Integration/Continuous Deployment (CI/CD) tools such as Jenkins
• Self-starter and self-motivated with the proven ability to deliver results in a fast-paced, high-energy environment
• 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.