• 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.

• Build and own end-to-end ML pipelines spanning data, training, evaluation, inference, and deployment. • Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation. • Architect and operate scalable inference systems, balancing latency, cost, and reliability. • Design and maintain data systems for high-quality synthetic and real-world training data. • Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. • Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies. • Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products. • Make pragmatic trade-offs and ship improvements quickly, learning from real usage. • Work under real production constraints: latency, cost, reliability, and safety.