Design, build, and deploy enterprise-grade autonomous AI services and multi-agent systems. Implement MCP and A2A integrations, architect hybrid control flows and state machines, integrate sandboxed tool execution with strict egress controls, and apply SRE principles for observability, fallback mechanisms, and reliability to move generative AI from POC to production.

Lead and grow a team of Agentic AI engineers to transition multi-agent and generative AI POCs into scalable, secure, production-ready services. Define technical roadmap, guide architecture for scalability and cost, champion agent interoperability standards, and establish CI/CD, observability, and delivery practices for LLM-powered applications that drive business impact.

Lead and mentor an AI engineering team to design, architect, and deploy production-ready multi-agent, LLM-powered systems. Define technical strategy, set architecture and security standards, implement hybrid control flows/state machines, integrate agent-to-tool and agent-to-agent protocols, and collaborate with product and business teams to translate requirements into scalable microservices.