• 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.