AI Agent Evals: Experience building eval frameworks for LLM agents (e.g., correctness graders, LLM-as-judge, human-in-the-loop evals, benchmark dataset curation); familiarity with agentic frameworks (Claude API/Anthropic SDK, BrainTrust, LangChain, LangGraph, CrewAI, or similar); production monitoring for AI systems (behavioral drift detection, output sampling, shadow scoring); red-teaming or adversarial testing experience for AI models or agents; experience with RAG pipelines, including data chunking and pre-retrieval data quality gates. You will own the architecture and strategy for the evaluation frameworks, automation tools, and testing infrastructure that give ID.me engineering teams the confidence to ship quickly and safely across functional, end-to-end, LLM/eval, and security-focused testing practices, as well as the CI/CD quality gates the org depends on.