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Join our cross-functional fraud detection squad where you'll build systems that protect millions of customers while keeping the good ones flowing seamlessly through our platform.
In this role you'll:
Build fraud detection systems using microservices in Typescript (NestJS) and Ruby on Rails, running over Kubernetes to catch bad actors while keeping legitimate customers happy
Leverage our autonomous tools to manage development flow-from environment setup to production deployment-so you spend time solving fraud, not fighting infrastructure
Collaborate with data scientists, product managers, and fraud analysts in a self-sufficient squad to hit aggressive fraud prevention OKRs
Own your domain's code quality, performance, and scalability as fraudsters get smarter and transaction volumes explode
Design systems that make split-second decisions on claims and applications, balancing security with customer experience
Coach and mentor other engineers while staying ahead of evolving fraud patterns and attack vectors
Partner with our AI/ML teams to implement and optimize machine learning models that detect fraud in real-time

Were seeking an experienced and skilled Data and AI Infra Tech Lead to join our Data Infrastructure team and drive the companys data capabilities at scale.
As the company is fast growing, the mission of the data and AI infrastructure team is to ensure the company can manage data at scale efficiently and seamlessly through robust and reliable data infrastructure.
As a tech lead, you are required to independently lead the design, development, and optimization of our data infrastructure, collaborating closely with software engineers, data scientists, data engineers, and other key stakeholders. You are expected to own critical initiatives, influence architectural decisions, and mentor engineers to foster a high-performing team.
You will:
Lead the design and development of scalable, reliable, and secure data storage, processing, and access systems.
Define and drive best practices for CI/CD processes, ensuring seamless deployment and automation of data services.
Oversee and optimize our machine learning platform for training, releasing, serving, and monitoring models in production.
Own and develop the company-wide LLM infrastructure, enabling teams to efficiently build and deploy projects leveraging LLM capabilities.
Own the company's feature store, ensuring high-quality, reusable, and consistent features for ML and analytics use cases.
Architect and implement real-time event processing and data enrichment solutions, empowering teams with high-quality, real-time insights.
Partner with cross-functional teams to integrate data and machine learning models into products and services.
Ensure that our data systems are compliant with the data governance requirements of our customers and industry best practices.
Mentor and guide engineers, fostering a culture of innovation, knowledge sharing, and continuous improvement.