Top AI & Machine Learning Jobs in San Francisco, CA

Design, build, and deploy production-grade autonomous multi-agent AI services and workflows. Implement agent communication protocols (MCP, A2A), hybrid control flows, secure sandboxed tool execution, and observability/SRE practices. Collaborate cross-functionally to translate requirements into scalable microservices using cloud foundation models.

Lead and grow an Agentic AI engineering team to move multi-agent and generative AI POCs into scalable, secure, production systems. Define technical roadmaps, ensure architecture and CI/CD/observability best practices, champion agent interoperability standards, and deliver autonomous LLM-powered services that meet business objectives.

Lead technical delivery of agentic multi-agent LLM systems: mentor engineers, define AI roadmap and architecture, design hybrid control flows/state machines, build scalable microservices, integrate MCP/A2A protocols and agent orchestration frameworks, and productionize solutions with cross-functional teams.

Support design, development, and productionization of autonomous multi-agent AI services. Assist with agent communication protocols (MCP/A2A), hybrid control flows, sandboxed tool execution, and scalable microservice architectures while collaborating with cross-functional teams to translate business needs into LLM-powered solutions.