Staff Forward Deployed Engineer, Developer AI, Google Cloud

Responsibilities

• Serve 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 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, spec-to-PR pipelines, and automated review/incident-to-fix loops.
• Embed with customers' Staff Engineers and Leaders to identify core SDLC bottlenecks, such as legacy migrations, test coverage, review latency, or onboarding friction, and define rigorous success metrics.
• Integrate Google’s agentic systems into the customer's existing ISV and tools (e.g., Atlassian Rovo/Teamwork Graph, GitLab, ServiceNow, Slack) leveraging MCP and A2A protocols.
• - Serve 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 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, spec-to-PR pipelines, and automated review/incident-to-fix loops. - Embed with customers' Staff Engineers and Leaders to identify core SDLC bottlenecks, such as legacy migrations, test coverage, review latency, or onboarding friction, and define rigorous success metrics. - Integrate Google’s agentic systems into the customer's existing ISV and tools (e.g., Atlassian Rovo/Teamwork Graph, GitLab, ServiceNow, Slack) leveraging MCP and A2A protocols.

Minimum qualifications:

Preferred qualifications:

Qualifications

• 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, 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, A2A) across third-party ISV platforms (e.g., Atlassian, 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.