Staff Data and ML Engineer ( - exp)

Team Summary:

The Risk and Identity Solutions (RaIS) team provides risk management services for banks, merchants, and other payment networks. Machine learning and AI models are the heart of the real-time insights used by our clients to manage risk. Created by the Visa Predictive Models (VPM) team, continual improvement and efficient deployment of these models is essential for our future success. To support our rapidly growing suite of predictive models we are looking for engineers who are passionate about managing large volumes of data, creating efficient, automated processes and standardizing ML/AI tools.

This is a great opportunity to work with a new Data Engineering and MLOps team to scale and structure large scale data engineering and ML/AI that drives significant revenue for Visa. As a member of the Risk and Identify Solutions modeling organization (VPM), your role will involve developing and implementing practices that will allow deployment of machine learning models in large data science projects.

You must be a hands-on expert able to navigate both data engineering and data science disciplines to build effective engineering solutions that support ML/AI models. You will partner closely with global stakeholders in RaIS Product, VPM Data Science and Visa Research to help create and prioritize our strategic roadmap. You will then leverage your expert technical knowledge of data engineering, tools and data architecture in the design and creation of the solutions on our roadmap.

The position is based at Visa's offices in Bangalore, India.

Essential Functions

• ETL processes: The role also involves developing and executing large scale ETL processes to support data quality, reporting, data marts, and predictive modeling.
• Spark pipelines: The role requires building and maintaining efficient and robust Spark pipelines to create and access data sets and feature stores for ML models.
• Strong Development experience in more than one of the following: Golang, Java, Python, Rust.
• Knowledge of standard Big data and Real Time stack such as Hadoop, Spark, Kafka, Redis, Flink and similar technologies
• Hands on experience in building and maintaining data pipelines, feature engineering pipelines and comfortable with core ML concepts
• Hands on experience in engineering, testing, validating and productizing AL/ML models for high performance use cases
• Exposure to model serving engines such as Tensorflow, Triton etc.
• Knowledge about DR / HA topologies, Reliability Engineering with hands on experience in implementing the same
• Knowledge of using and maintaining DevOps tools and implementing automations for production
• Experience of working with containerized and virtualized environments (Docker, K8s)
• Exposure to public cloud equivalents, and ecosystem shall be a plus

This is a hybrid position. Hybrid employees can alternate time between both remote and office. Employees in hybrid roles are expected to work from the office 2-3 set days a week (determined by leadership/site), with a general guidepost of being in the office 50% or more of the time based on business needs.