Forward Deployed Engineer IV, GenAI, Google Cloud
US: $207000 - $301000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
- 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 (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 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 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.
- 8 years of experience with software development using Python or similar coding languages.
- Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions.
- Experience taking production-grade AI-driven solutions from conception to launch for customers.
- Experience architecting AI systems on cloud platforms (e.g. Google Cloud Platform (GCP)).
- Experience leading technical discovery sessions with customers.
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 Model (LLM) native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.