Design and build Machine Learning platform components that support agentic systems, including retrieval pipelines, indexing strategies, and model integration layers. Introduce and operationalize RAG use cases, from data sourcing and embedding generation to runtime retrieval patterns. Develop generalized evaluation frameworks for LLM- and agent-based features, including offline metrics, golden datasets, and continuous monitoring. Implement abstractions, tooling, and reusable patterns that enable other teams to build ML- and LLM-powered experiences efficiently. Partner with backend engineers to productionize ML features with strong reliability, observability, and performance characteristics. Prototype applied ML solutions to validate feasibility before investing in full builds. Ensure secure, robust handling of data used in ML workflows and retrieval operations. Collaborate with product, design, and engineering to align ML system design with user experience and product goals. Contribute to iterative improvements of the Nova agent framework, including workflows built with Mastra and TypeScript.