- Design, develop, and maintain scalable data pipelines to support ingestion, transformation, and delivery into centralized feature stores, model-training workflows, and real-time inference services. - Build and optimize workflows for extracting, storing, and retrieving semantic representations of unstructured data to enable advanced search and retrieval patterns. - Architect and implement lightweight analytics and dashboarding solutions that deliver natural language query experience and AI-backed insights. - Define and execute processes for managing prompt engineering techniques, orchestration flows, and model fine-tuning routines to power conversational interfaces. - Oversee vector data stores and develop efficient indexing methodologies to support retrieval-augmented generation (RAG) workflows. - Partner with data stakeholders to gather requirements for language-model initiatives and translate into scalable solutions. - Create and maintain comprehensive documentation for all data processes, workflows and model deployment routines. - Should be willing to stay informed and learn emerging methodologies in data engineering, MLOps and LLM operations.

• Design, build, and maintain backend APIs and services supporting credit risk and fraud decisioning • Deploy and operate machine learning models, scorecards, and business rules in production • Own system reliability, performance, and observability for underwriting and fraud services • Debug and resolve production issues affecting decision accuracy or service availability • Partner closely with data scientists to productionize models and ensure safe, repeatable deployments • Improve system architecture to support scalability, fault tolerance, and operational robustness • Implement monitoring and alerting for model behavior, decision outcomes, and system health • Collaborate with Credit Risk Strategy, Risk Modeling, Tech, and Operations teams to translate strategy into live decisions • Develop deep expertise in Katapult’s credit risk and fraud infrastructure

• Design Generative AI solutions, such as information retrieval and summary generation for support and operations organizations. • Partner with Software Engineering teams to build and deploy GenAI applications. • Ideate and explore opportunities to deploy GenAI technologies for customer-facing experiences • Drive the adoption and scalability of Generative AI capabilities within SmithRx; advocacy on the technology and evangelize the use of GenAI.

• Design, develop, and maintain robust backend applications and services to serve ML inference (FastAPI, Flask, or Node.js); • Build and optimize pipelines for real-time or batch inference processing; • Monitor and optimize model performance in production, ensuring low latency and high availability; • Design distributed systems capable of supporting intensive Machine Learning workloads; • Work closely with data scientists and ML engineers to translate research models into production-ready services; • Identify and integrate emerging technologies to improve system performance and the end-user experience.