• Drive strategic AI initiatives that directly impact client success and business growth, defining technical roadmaps and influencing product strategy to solve complex enterprise problems. • Architect and enhance our agentic processes for enterprise-scale deployments, building sophisticated multi-agent orchestration patterns for complex workflows. • Design advanced agent memory systems and context management solutions that maintain coherence across long-running conversations and extended enterprise tasks. • Build and implement RAG (Retrieval-Augmented Generation) systems to dramatically improve AI accuracy, including knowledge retrieval pipelines and semantic search optimization for large-scale datasets. • Develop enterprise-grade MCP (Model Context Protocol) services enabling seamless client agent integration with standardized APIs, security protocols, and comprehensive documentation. • Leverage AWS technologies (Bedrock, Lambda, etc) to architect AI solutions with optimal performance, cost efficiency, and enterprise-scale LLM integration. • Design and optimize schemas for storing LLM interactions, agent state, and conversation history while building monitoring systems for AI operations. • Lead cross-functional initiatives to integrate AI throughout our platform ecosystem, partnering with product and engineering teams to deliver measurable business value. • Translate complex technical AI concepts into business value, working directly with enterprise clients to understand their needs and influence strategic platform decisions. • Mentor engineering teams on AI best practices, emerging technologies, and enterprise AI governance while maintaining high engineering standards for production AI systems.