Google Cloud is hiring aStaff Forward Deployed Developer, Generative AIto join its Go-To-Market team in Toronto, Ontario. This is a senior technical role for someone who functions as an "innovator-builder," moving beyond high-level architecture to code, debug, and jointly ship bespoke agentic solutions directly within the customer's environment. The Canada base salary range for this full-time position is CAD 216,000–221,000 + bonus + equity + benefits. ResponsibilitiesServe as a developer for complex AI applications, transitioning from rapid prototypes to production‑grade agentic workflows (e.g., multi‑agent systems, MCP servers) that drive measurable ROI. Architect and code the "connective tissue" between Google's AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters. Build evaluation pipelines and observability frameworks to ensure agentic systems meet rigorous requirements for accuracy, safety, and latency. Identify repeatable field patterns and friction points in Google's AI stack, converting them into reusable modules or formal product feature requests for the Development teams. Co‑build with technical sales teams to instil Google‑grade development best practices, ensuring long‑term project success and high end‑user adoption. Minimum QualificationsBachelor's degree in Engineering, Computer Science, a related field, or equivalent practical experience. 8 years of experience in cloud computing or a technical customer‑facing role. Experience building pipelines for structured and unstructured data using both vector databases and RAG‑like architectures to power enterprise AI solutions. Experience taking production‑grade AI‑driven solutions from conception to launch and architecting AI systems on cloud platforms (e.g., GCP). Experience leading technical discovery sessions. Preferred QualificationsMaster's or PhD in AI, Computer Science, or a related technical field. Experience implementing multi‑agent systems using frameworks (e.g., LangGraph, CrewAI, ADK) and complex patterns (e.g., ReAct, self‑reflection, hierarchical delegation). Knowledge of LLM‑native metrics (e.g., tokens/sec, cost‑per‑request) and techniques for optimizing state management and granular tracing.#J-18808-Ljbffr