Serve as the lead developer for AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, Model Context Protocol (MCP) servers) that drive 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.
Build evaluation pipelines and observability frameworks to ensure agentic systems meet requirements for safety, and latency.
Identify repeatable field patterns and technical friction points in Google’s AI stack, converting them into reusable modules or formal product feature requests for the engineering teams.
Co-build with customer engineering teams to instill Google-grade development best practices, ensuring project success and end-user adoption.
Minimum qualifications:
Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience
6 years of experience shipping production-grade AI-driven solutions to external or internal customers.
Experience architecting scalable AI systems on cloud platforms (e.g., Google Cloud Platform).
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 (LLM)-native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
Ability to implement agentic workflows incorporating MCP, tool-calling, and OAuth-based authentication.
