Staff Forward Deployed Engineer, GenAI, Google Cloud

• Lead the discovery-to-deployment journey, serving as the lead developer for AI applications and transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems and MCP servers) that drive measurable return on investment.
• Bridge the enterprise gap by architecting and coding the connective tissue between Google’s AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters.
• Engineer for production excellence, building high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet the requirements for accuracy, safety, and latency.
• Act as a product catalyst, identifying repeatable field patterns and technical friction points in Google’s AI stack and converting them into reusable modules or product feature requests for engineering teams.
• Drive regional leadership and upskilling by mentoring talent, co-building with customer teams, and influencing cross-functional strategies to uplevel organizational technical capabilities.

Minimum qualifications:

• Bachelor’s degree in Science, Technology, Engineering, Mathematics, a related technical field, or equivalent practical experience.
• 8 years of experience shipping production-grade AI solutions to external or internal customers.
• Experience building full-stack solutions that interface with enterprise systems.
• Experience in Python.
• Experience architecting AI systems on cloud platforms.

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, or Google’s Agent Development Kit (ADK)) and patterns like ReAct, self-reflection, and hierarchical delegation.
• Experience in the telecommunication, manufacturing or retail industry.
• Knowledge of large language model-native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
• Ability to implement secure agentic workflows incorporating Model Context Protocol (MCP), tool-calling, and OAuth-based authentication.

• Bachelor’s degree in Science, Technology, Engineering, Mathematics, a related technical field, or equivalent practical experience.
• 8 years of experience shipping production-grade AI solutions to external or internal customers.
• Experience building full-stack solutions that interface with enterprise systems.
• Experience in Python.
• Experience architecting AI systems on cloud platforms.