• Architect and scale the infrastructure that powers AI quality, reliability, and reuse across Lattice. • Design and scale an end-to-end AI evaluation framework spanning offline evals, production tracing, and human feedback loops. • Define meaningful performance metrics (task completion, hallucination, response quality, engagement, business impact) and build the datasets and automated scoring systems that prevent regressions. • Identify and quantify the drivers of agent quality improvement and set methodological standards for evaluation across the organization. • Architect reusable agent infrastructure (multi-turn workflows, LLM DAGs, recommendation systems, standardized topologies) using LangGraph or comparable frameworks. • Build and scale RAG pipelines, vector retrieval systems, and production-grade AI infrastructure with strong reliability, observability, and performance. • Make principled build-vs-buy decisions across LLM providers, agent frameworks, and evaluation tooling, balancing capability, cost, latency, and risk. • Engineer AI systems as reusable internal platforms that multiply product engineering velocity at Lattice. • Own projects end-to-end: scope, design, execution, and delivery. • Set technical direction for agent quality and evaluation strategy across Lattice engineering teams. • Lead rigorous discussions on AI system design and evaluation methodology. • Raise the AI engineering bar through mentorship, code review, and clear technical communication across engineering and leadership.