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.
- 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
