Forward Deployed Engineer III, Generative AI, Google Cloud (English, Spanish)
Summary
Embeds with enterprise customers to design, code, and deploy production-grade generative AI systems, bridging Google Cloud’s AI stack with live customer infrastructure.
- 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.
- 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 the 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 the 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:
- 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 architecting AI systems on cloud platforms (e.g., Google Cloud Platform).
- Experience building pipelines for structured and unstructured data using both vector databases and Retrieval-augmented generation (RAG)-like architectures to power enterprise AI solutions.
- Experience taking production-grade AI-driven solutions from conception to launch for customers.
- Ability to communicate in English and Spanish fluently to support client relationship management in this region.
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).
- Knowledge of "Large Language Models (LLMs)-native" metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.