AI Engineer

Join our team as an AI Engineer, where you will serve as a technical architect for our Intelligence Layer and Data Consortium. This specialized engineering role focuses on building high-scale systems that power our fraud intelligence. You will design, build, and operate distributed services and data pipelines, own and evolve our internal AI agent workflow, and play a key role in building AI applications using state-of-the-art large language models. This is a production software engineering role, and while classic ML modeling experience is a plus, strong software engineering fundamentals and a solid understanding of ML concepts are essential.

• Design, build, and operate distributed, production-grade services and data pipelines that ingest real-time signals from millions of users.
• Own and evolve the internal AI agent workflow and tooling originally prototyped by the detection team, and help migrate it onto the new, production-grade agent framework.
• Collaborate with Data Scientists to deploy and maintain pipelines for both Unsupervised (UML) and Supervised (SML) models, integrating them with APIs to enable real-time scoring and decisioning.

• Proven ability to design and implement distributed, cloud-native systems for high-throughput, low-latency applications. Experience with AWS and containerization (Docker/Kubernetes) is required
• Strong, production-grade skills in Python (primary language for services and tooling), plus experience with at least one additional lower-level programming language such as Java (Go or C++ also a plus)
• System Architecture
• Education
• Big Data Technologies
• Collaboration & Ownership
• 2+ years of professional software engineering experience building and shipping production systems (backend services, data platforms, or ML/AI infrastructure), ideally for customer-facing SaaS products or internal platform tools
• Solid breadth and depth in ML concepts (e.g., supervised vs. unsupervised learning, feature engineering, embeddings, evaluation metrics like Precision/Recall and AUC), even if you have not been the primary model owner on a team
• Bachelor’s and Master’s degree in Computer Science (or a closely related field) with a focus in Machine Learning or Artificial Intelligence
• Demonstrated ability to work cross-functionally, take end-to-end ownership of services, and operate in a fast-paced, high-impact environment
• Coding Proficiency
• Hands-on experience with distributed data frameworks such as Spark, Kafka, or Flink
• Machine Learning Foundations
• Experience building or integrating LLM-powered agent workflows (e.g., LangChain/LangGraph, multi-agent orchestration, tool calling, or RAG architectures) for production or internal platforms
• Experience deploying or maintaining machine learning models (supervised or unsupervised) in production environments
• Experience working with analytical databases and large-scale data exploration (e.g., ClickHouse or other columnar data stores) is a plus
• Domain knowledge in fraud or risk (fraud detection, credit risk, payments, or trust & safety) is a strong plus but not required
• Background in real-time decision engines or stateful stream processing