* Drive the full lifecycle of agent implementation, from initial design to a live, high-performing production environment using RAG, MCP, and agentic architectures * Test, evaluate, and fine-tune AI agents, RAG pipelines, and Model Context Protocol (MCP) implementations to hit strict performance targets using real-world data. * Implement and optimize REST APIs for seamless system integration * Debug, troubleshoot, and support production systems in real-time * Define and enforce best practices for system design, including scalability, fault tolerance, and performance optimization. * Work closely with frontend developers, DevOps engineers, product managers, and other stakeholders to deliver end-to-end solutions that meet business requirements. * Create and maintain technical documentation for architecture, APIs, and processes to facilitate knowledge sharing and onboarding. * Deploy and manage containerized services on Google Kubernetes Engine (GKE) or Cloud Run. * Implement deep tracking and monitoring for non-deterministic AI agent behaviors using Google Cloud Observability (Stackdriver) or OpenTelemetry.