Software Development Manager, Payment Risk Engineering
Amazon
Software Engineering
India · Bengaluru, Karnataka, India · Karnataka, India
Description
Enabling Amazon's explosive growth requires top talent in our Amazon Buyer Risk Technologies. Amazon is seeking a Software Development Manager to lead a technical team within Payment Risk Engineering, focused on two critical areas: (1) automating the launch of new countries, payment methods, and store concepts, and (2) feature engineering for ML models used in our fraud evaluation pipeline.
Our organization is building secure buyer risk and fraud prevention systems that need to scale to the exponential growth in Amazon products. We are leveraging machine learning to build systems that understand our account holder's behavior and can detect changes in that pattern. We are building systems that leverage unique storage systems such as graph database technologies. We are building on a sophisticated rules management platform to give our machine learning scientists and investigators control over risk calculations.
As a leader in this space, you will drive the adoption of Generative AI to accelerate how we build and ship fraud prevention capabilities. This includes leveraging GenAI to automate feature creation for our ML models — from feature discovery and proposal through validation and integration into the fraud evaluation pipeline — as well as using GenAI-powered tooling to reduce the manual effort required to onboard new marketplaces, payment methods, and store concepts. You will set the vision for how GenAI transforms our engineering workflows and empower your team to stay at the forefront of these technologies.
Because you take pride in setting the standard for engineering excellence, you are a hands-on, pragmatic problem solver that easily balances trade-offs between competing interests. You thrive in a fast-moving team environment where you are able to juggle complex dependencies and requirements while producing optimal solutions. Ambiguity and creativity are both expected and the norm.
We make the world a better place by finding the bad people, and make it easier for normal sellers and buyers to conduct their business.