• Design, develop, and maintain high-performance, scalable, and secure backend services, primarily using Python and frameworks like FastAPI • Translate ambiguous business and technical requirements into concrete software designs and actionable tasks for cross-functional teams • Work with OLTP databases, including schema design, migration strategies, and performance optimization • Integrate SmartAsset services with internal and external APIs, including third-party platforms and existing AWS infrastructure • Ensure secure ingress and egress of data within our private network environments • Contribute to the design and implementation of our CI/CD pipelines to ensure continual improvements to our cycle time • Manage and optimize AWS services, including IAM roles and policies • Gain a deep understanding of how our Python applications integrate with our Kubernetes (EKS) and future ECS-based infrastructure • Operate and maintain production applications at scale, ensuring high availability, performance, and reliability • Design and implement data collection pipelines specifically for Large Language Models (LLMs) • Contributed to shared data architecture and data governance practices to ensure the persistence of structured data suitable for analytics and model training • Explore, integrate, and tune various LLM-related technologies, such as Retrieval Augmented Generation (RAG) systems, and addressing potential scaling challenges • Investigate and implement agentic technologies, such as tool calling frameworks (e.g., AutoGen), to enhance LLM capabilities • Rapidly prototype user interfaces for internal tools and data exploration, leveraging frameworks like Streamlit, Gradio, or other similar tools that facilitate quick iteration and integration without persistent websockets

• Design, develop, and deploy machine learning models to solve business problems across large-scale datasets. • Build and optimize machine learning pipelines for data preparation, model training, and inference. • Collaborate with data engineers and software engineers to develop scalable ML infrastructure and pipelines. • Research and implement modern machine learning techniques, including deep learning and large language models where appropriate. • Work closely with product and cross-functional teams to translate business requirements into technical solutions. • Deploy and maintain machine learning models in production environments. • Monitor model performance, conduct experiments and A/B testing, and continuously improve model accuracy and reliability. • Contribute to the team's engineering best practices, including code reviews, documentation, and knowledge sharing.

• Design, develop, and deploy machine learning models to solve business problems across large-scale datasets. • Build and optimize machine learning pipelines for data preparation, model training, and inference. • Collaborate with data engineers and software engineers to develop scalable ML infrastructure and pipelines. • Research and implement modern machine learning techniques, including deep learning and large language models where appropriate. • Work closely with product and cross-functional teams to translate business requirements into technical solutions. • Deploy and maintain machine learning models in production environments. • Monitor model performance, conduct experiments and A/B testing, and continuously improve model accuracy and reliability. • Contribute to the team's engineering best practices, including code reviews, documentation, and knowledge sharing.

• Act as a Player-Coach: Dive deep into the data and code. You will spend a significant portion of your time making direct technical contributions, reviewing code/PRs, and understanding the mathematical nuances of your team's models. • Lead Strategic Initiatives: Take ownership of a specific product area (like Cash Line or Auto) and serve as the de facto ML leader in cross-functional strategy meetings. • Drive 0→1 and Scaling ML Efforts: Depending on your team placement, you may build out entirely new capabilities from scratch or optimize highly mature models dealing with massive scale and shifting macro-economic regimes. • Translate Models to Business Impact: Design and refine decision engines that translate model predictions into accurate, transparent, and customer-friendly lending outcomes.