• Design and build the frontend systems that power a brand-new consumer credit experience. • Develop scalable, distributed services using event-driven architectures to ensure reliability, observability, and long-term maintainability. • Collaborate closely with design and product to translate intent into features that serve millions of consumers. • Drive technical decisions in a zero-to-one product environment, balancing speed with high-quality execution. • Apply AI-native workflows to accelerate development, testing, analysis, and documentation. • Help define engineering best practices and culture within a fast-moving and outcome-driven team.

• Build and maintain robust infrastructure using tools like Terraform, Azure, and GitLab CI/CD pipelines. • Implement best practices for continuous integration, deployment, and monitoring to ensure high availability and reliability of our applications. • Work with cross-functional teams (including data scientists) to design and deploy scalable solutions that handle large volumes of data efficiently. • Optimize system performance, ensuring quick response times and efficient resource utilization. • Implement security best practices and ensure compliance with industry standards for data protection. • Write clean, scalable, and efficient code using C# and the .NET framework. • Develop, test, deploy, and maintain software applications and systems. • Revise, update, refactor, and debug existing code and applications.

• Infrastructure Development: Build and maintain Enterprise Feature Stores and real-time data lakes (BigQuery, Pub/Sub) to streamline predictive modeling and NLP workflows. • Design and implement resilient, secure multi-cloud architectures (GCP and Azure) using Terraform (IaC) to support high-availability AI operations. • Operationalize ML models using Python, PySpark, and Kafka for both batch and real-time inference. • Leverage advanced orchestration frameworks such as LangChain, LangGraph, CrewAI, and Vertex AI Agent Builder to develop sophisticated agentic AI solutions. • Implement end-to-end CI/CD pipelines for automated model training, deployment, and seamless rollback capabilities. • Standardize LLMOps and MLOps observability practices using tools like LangSmith and Arize platforms. • Collaborate with data scientists to understand model objectives and translate them into technical specifications. • Deploy AI models into multiple environments using CI/CD pipelines, ensuring seamless integration with existing systems. • Establish strong observability and data governance using tools like Grafana, OpenTelemetry, Splunk or Arize to monitor pipeline health and model behavior. • Implement security measures to protect sensitive data and ensure compliance with industry regulations.

• Own team-level architecture, delivering strategic and tactical software to address defined problems • Begin to influence work outside of your direct team, solving complex problems impacting performance, features, and product design • Support and unblock peers through async reviews, pairing, and mentorship • Work in an async-first way, aligning clearly on scope, tradeoffs, and outcomes • Build across the stack, using TypeScript/React/Node with PostgreSQL, Redis, and Kubernetes under the hood