Primary Responsibilities
Consortium Data Engineering
- Architect and maintain high-throughput data pipelines (using technologies such as 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
- Design and 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.
AI Agent Workflow Ownership
- Own and extend the internal AI agent tool and workflows used by the Solutions team for rule and feature creation, rule tuning, and alert analysis, ensuring reliable deployments across sandbox, preprod, and production solution tenants.
Label & Rule Tuning Automation
- Build map-reduce style LLM workflows and analytics pipelines (e.g., ClickHouse, Spark) for large-scale label investigation, weak classifier discovery, and FN/FP triage to accelerate solution onboarding and improve detection coverage.
Productionize ML Pipelines
- Collaborate with Data Scientists to deploy and maintain pipelines for both Unsupervised (UML) and Supervised (SML) models, integrating them with our APIs to enable real-time scoring and decisioning. Hands-on ownership of classic ML modeling is a plus, but not a strict requirement.
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–driven features and AI-powered workflows.
Requirements
Experience
- 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.
Education
- Bachelor’s and Master’s degree in Computer Science (or a closely related field) with a focus in Machine Learning or Artificial Intelligence.
System Architecture
- 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.
Coding Proficiency
- 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).
Big Data Technologies
- Hands-on experience with distributed data frameworks such as Spark, Kafka, or Flink.
Machine Learning Foundations
- 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.
Collaboration & Ownership
- Demonstrated ability to work cross-functionally, take end-to-end ownership of services, and operate in a fast-paced, high-impact environment.
Preferred Qualifications
- 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).
- Background in real-time decision engines or stateful stream processing.
- Domain knowledge in fraud or risk (fraud detection, credit risk, payments, or trust & safety).
Benefits
- Base Salary: 130K – 250K
- Total Compensation: Includes Base + Performance Bonus + Equity Options.
- Comprehensive medical, dental, and vision coverage.
- Discretionary Time Off Policy (DTO) and paid holidays.
- Opportunities for R&D exploration and professional development.
- Regular team-building events and a collaborative, innovative culture.