Staff Forward Deployed Engineer, Developer AI, Google Cloud

• Serve as a developer for complex AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, Model Context Protocol (MCP) servers) that drive measurable Return on Investment (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 as part of an expert team.
• Design and deploy production-grade agentic developer workflows on Google Cloud's AI stack, executing large-scale refactors, language migrations, Specification-to-Pull Request pipelines, and automated review/incident-to-fix loops.
• Embed with customer's staff engineers and leaders to identify core SDLC bottlenecks, such as legacy migrations, test coverage, review latency, or onboarding friction, and define success metrics.
• Integrate Google’s agentic systems into the customer's existing ISV and tools (e.g., Teamwork Graph, GitLab, ServiceNow, Slack) leveraging MCP and A2A protocols.

Minimum qualifications:

• Bachelor’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 deploying, scaling, and debugging Large Language Model (LLM) or agent-based systems in production environments (including tools, memory, orchestration, evaluation, tracing, and cost/latency profiling).
• Experience with end-to-end technical ownership of engineering projects with executive stakeholders.

Preferred qualifications:

• Master’s degree or PhD in AI, Computer Science, or a related technical field.
• Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, Agent Development Kit (ADK)) and complex patterns (e.g., ReAct, self-reflection, hierarchical delegation).
• Experience with agentic frameworks and harness layers, such as Google's Agent Development Kit (ADK) or equivalent, protocol-level interoperability (MCP, Agent-to-Agent (A2A)) across third-party Independent Software Vendor (ISV) platforms (e.g., ServiceNow), and security ecosystem in DevSecOps.
• Knowledge of LLM-native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.

• Bachelor’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 deploying, scaling, and debugging Large Language Model (LLM) or agent-based systems in production environments (including tools, memory, orchestration, evaluation, tracing, and cost/latency profiling).
• Experience with end-to-end technical ownership of engineering projects with executive stakeholders.