security, architecture, data engineering, and product teams to convert operational problems into reusable graph-powered capabilities. • Own technical design reviews, coding standards, model validation, observability, performance tuning, security controls, documentation, and production-readiness gates. • Mentor data scientists and engineers while remaining accountable for prototypes, reference implementations, critical code, troubleshooting … vocabularies. • GenAI and GraphRAG: LLM-based extraction, retrieval orchestration, agent/tool integration, prompt design, evaluation, grounding, guardrails, explainability, and evidence traceability. • MLOps and observability: Experiment tracking, model versioning, deployment, monitoring, drift and quality checks, auditability, access controls, secrets management, and cost/performance management. Security Knowledge Graph Engineering Expectations ...
