Forward Deployed Engineer, Higher Education, Google Public Sector

Google•Sunnyvale, CA

2d•$207,000 - $300,000

About The Position

The Google Public Sector Forward Deployed Engineering team rapidly deploys production-grade, secure AI solutions across Federal and state, local, and education environments. Engineers actively code, debug, and co-build bespoke agentic workflows alongside customers, resolving complex integration, data sovereignty, and security issues. The team supports safe, reliable adoption of generative AI in mission-critical operations and feeds field insights back to Google Cloud Product engineering. This role focuses on Higher Education customers. The engineer develops complex AI applications, connects Google AI products with customer infrastructure, and builds evaluation and observability capabilities. The role also identifies repeatable field patterns and collaborates with customer engineering teams to support lasting project success and end-user adoption.

Requirements

Bachelor's degree in Computer Science, Engineering, a related field, or equivalent practical experience.
At least 8 years of experience building and shipping production-grade AI-driven solutions for external or internal customers using Python, TypeScript, or comparable languages.
Experience building scalable pipelines for structured and unstructured data, incorporating vector databases and RAG-like architectures for enterprise-grade AI solutions.
Experience leading technical discovery sessions with C-suite stakeholders and engineering teams to define AI and hardware infrastructure requirements.
Experience architecting scalable AI systems on cloud platforms.

Nice To Haves

Master's degree or PhD in AI, Computer Science, or a related technical field.
Experience implementing multi-agent systems using frameworks such as LangGraph, CrewAI, or Google's ADK, and patterns such as ReAct, self-reflection, and hierarchical delegation.
Experience architecting integrated systems, navigating real-time inference constraints, and implementing model quantization for resource-constrained environments.
Experience partnering with Higher Education customers, specifically R1 institutions, to drive digital transformation and AI readiness.
Knowledge of LLM-native metrics such as tokens per second and cost per request, and techniques for optimizing state management and granular tracing.
Proficiency in Vertex AI Pipelines, Kubeflow, or MLflow for CI/CD/CT automation and experimentation.

Responsibilities

Develop complex AI applications, moving rapid prototypes into production-grade agentic workflows, such as multi-agent systems and MCP servers, that drive measurable ROI.
Architect and code integrations between Google AI products and customer 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 accuracy, safety, and latency requirements.
Identify repeatable field patterns and friction points in Google's AI stack, and convert them into reusable modules or formal product feature requests for Engineering teams.
Co-build with customer engineering teams and promote Google-grade development best practices to support long-term project success and end-user adoption.