AI/ML Engineer, DataVisor

• Experience: 1–5 years of experience in Machine Learning Engineering, Data Engineering, or Backend Engineering
• System Architecture: Proven ability to design distributed, cloud-native systems for high-throughput applications. Experience with AWS and containerization (Docker/Kubernetes) is critical
• Big Data Tech: Strong hands-on experience with distributed data frameworks such as Spark, Kafka, or Flink
• Coding Proficiency: Production-grade skills in Python and at least one compiled language (e.g., Java or C++)

Desirable

• We are hiring an AI/ML Engineer to serve as a technical architect for our Intelligence Layer and Data Consortium. This is a specialized engineering role—distinct from general web development—focused on building the high-scale "muscle" that powers our fraud intelligence
• You will design and maintain distributed pipelines that ingest real-time signals from millions of users, and engineer backend systems that enable our Agentic Flow to "auto-tune" strategies
• You will also play a key role in building agentic flows and AI applications using state-of-the-art, out-of-the-box large language models (LLMs) available on the market, in addition to helping build and deploy traditional machine learning models
• Consortium Data Engineering: Architect and maintain high-throughput data pipelines (using Spark, Kafka, or Flink) to ingest, process, and aggregate real-time signals—such as device fingerprints and behavioral biometrics—into our central intelligence graph
• High-Scale System Design: Optimize distributed systems to support our global data network, ensuring the platform can handle 10,000+ Transactions Per Second (TPS) with P99 latency under 150ms
• Agentic Flow & AI Application Development: Build agentic flows and AI applications by leveraging state-of-the-art, out-of-the-box LLMs (e.g., OpenAI, Anthropic, Google) to enable natural language interaction, intelligent rule merging, and automated fraud strategy recommendations
• Productionize ML Pipelines: Deploy and maintain pipelines for both Unsupervised (UML) and Supervised (SML) models, integrating them with our API to enable real-time scoring and decisioning
• Privacy-First Architecture: Implement robust security measures, including tokenization and hashing, to ensure PII privacy and compliance across our shared intelligence network
• Cross-Functional Collaboration: Work closely with Data Science, Product, Strategy, Delivery, and Engineering teams to develop, validate, and optimize machine learning models and AI-driven features