Partner Forward Deployed Engineer, GenAI, Google Cloud
Summary
Build and deploy production-grade generative AI systems with Google Cloud partners, transitioning prototypes into scalable agentic solutions while solving integration and data challenges.
- Serve as a team lead and developer within the strategic AI partner for complex AI applications, working with the partner’s own teams to transition from rapid prototypes to production-grade, replicable agentic workflows (e.g., multi-agent systems, MCP servers) that drive measurable return on investment (ROI).
- Build high-performance evaluation pipelines and observability frameworks to ensure partner developed agentic systems meet rigorous requirements for accuracy, safety, and latency.
- Identify repeatable partner and 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 a strategic AI partner’s forward deployed engineering teams to instill Google-grade development best practices.
- Help partners to build their own agentic delivery capabilities to set them up for long term success, focusing on the ROI at customer engagements ensuring customer activation.
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 solutions from conception to launch for customers.
- 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, Agent Development Kit (ADK)) and complex patterns (e.g., ReAct, self-reflection, 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.