Senior Forward Deployed Engineer, GenAI, Google Cloud
Summary
A Senior Forward Deployed Engineer at Google Cloud acts as an embedded builder-consultant in Singapore, coding and shipping production-grade GenAI solutions (agentic workflows, MCP servers, evaluation pipelines) directly within customer environments using Python, Vertex AI, and Gemini models.
- 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 Model Context Protocol 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 by mentoring talent, co-building with customer teams, and influencing cross-functional strategies to up-level 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.
- 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.