• Lead structured discovery with practice leaders/experts to understand workflows, data, pain points, and opportunities for AI-driven automation and improved deliverables. • Translate expert needs into clear product requirements, user stories, success metrics, and implementation plans to execute. • Own and maintain an AI capability roadmap focused on AI workflows, agents, and practice-specific tools aligned with BRG strategy and compliance. • Prioritize AI use cases based on impact, feasibility, risk, supportability, and measurable value (efficiency, quality, new offerings). • Drive adoption: build enablement plans, gather feedback, track usage metrics, and iterate to improve sustained value. • Design and ship production AI capabilities such as RAG, prompt/tool patterns, and agent workflows with end-to-end ownership (design → build → test → deploy → monitor). • Implement and improve retrieval quality (chunking, embeddings, hybrid/semantic ranking, prompt design) and establish evaluation approaches (offline/online testing and human-in-the-loop where needed). • Integrate Azure AI services end-to-end (e.g., Azure OpenAI, Azure AI Search, Document Intelligence, orchestration frameworks) into secure and supportable solutions. • Operationalize solutions using CI/CD, telemetry/monitoring, rollout strategies, and reliability targets (SLIs/SLOs) for production readiness. • Provide Tier III support: troubleshoot incidents, perform root cause analysis, implement fixes, and create runbooks for support handoff.