What are the responsibilities and job description for the Lead Machine Learning Engineer position at Visa?
VISA is the leader in the payment industry and has been for a long time, but we are also quickly transitioning into a technology company that is fostering an environment for applying the newest technology to tackle exciting problems in this area. For a payment system to work well, the risk techniques, performance, and scalability are crucial. These techniques and systems benefit from big data, data mining, artificial intelligence, machine learning, cloud computing, & many other advance technologies. At VISA, we have all of these. If you want to be on the cutting edge of the payment space, learn fast, and make a huge impact, then the Artificial Intelligence Platform team may be an ideal place for you!
Visa AI as a Service (VAIaS) operationalizes the delivery of AI and decision intelligence to ensure their ongoing business values. Built with composable AI capabilities, privacy-enhancing computation, and cloud native platforms, VAIaS automates the updates to data, models, and applications. Combined with strong AI governance, VAIaS optimizes the performance, scalability, interpretability and reliability of AI models and services. If you want to be in the exciting payment and AI space, learn fast, and make big impacts, Visa AI as a Service is an ideal place for you!
Essential Functions:
Our team needs a Lead ML Engineer with strong Data and Machine Learning System development and operations experience, who will contribute to both strategic and tactic planning and execution to continuously advance AI Platform's vision and mission. In this position, you are first a hard-working and versatile data engineering leader that can work in a multifaceted environment as a member of Agile scrum teams.
You will be an integral part of the leadership team, setting up technical roadmap, introducing the latest machine learning and system technology to the team, working with development managers and teams to identify and resolve technical challenges, and working with different products and data science teams to streamline the feature request and model onboarding.
This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager.