Forward Deployed Engineer, Generative AI, Geo

Responsibilities

• 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.
• 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.
• Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet rigorous requirements for accuracy, safety, and latency.
• Identify repeatable field patterns and friction points in Google’s AI stack, converting them into reusable modules or formal product feature requests for engineering teams.
• Co-build with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.
• - 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. - 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. - Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet rigorous requirements for accuracy, safety, and latency. - Identify repeatable field patterns and friction points in Google’s AI stack, converting them into reusable modules or formal product feature requests for engineering teams. - Co-build with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.

Minimum qualifications:

Preferred qualifications:

Qualifications

• Minimum qualifications: - Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience. - 5 years of experience with software development using Python or similar coding languages. - Experience taking production-grade AI-driven solutions from conception to launch for customers. - Experience managing technical discovery sessions with customers. - Experience building pipelines for structured and unstructured data using both vector databases and Retrieval-Augmented Generation (RAG)-like architectures to power enterprise AI solutions. 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 patterns (e.g., ReAct, self-reflection, hierarchical delegation). - Knowledge of LLM-native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.