Forward Deployed Engineer, Higher Education, Google Public Sector

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 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 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 Computer Science, Engineering, a related field, or equivalent practical experience.8 years of experience building and shipping production-grade AI-driven solutions to external or internal customers using Python, TypeScript or comparable languages.Experience building scalable pipelines for structured, unstructured data, incorporating vector databases and RAG-like architectures to power enterprise-grade AI solutions.Experience leading technical discovery sessions with executive stakeholders (C-suite) and engineering teams to define AI and hardware infrastructure requirements.Experience architecting scalable AI systems on cloud platforms.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, or Google’s ADK) and complex patterns like ReAct, self-reflection, and hierarchical delegation.Proven experience architecting integrated systems, navigating real-time inference constraints, and implementing model quantization for resource-constrained environments.Proficiency in Vertex AI Pipelines, Kubeflow, or MLflow to implement robust CI/CD/CT automation and experimentation.Knowledge of "LLM-native" metrics (tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.Designing resilient data engineering pipelines using BigQuery and VertexAI for enterprise-scale analytics.The Google Public Sector Forward Deployed Engineering (GPS FDE) team is a squad of "innovator-builders" who rapidly deploy production-grade, secure AI solutions across Federal and SLED environments. Operating with a high-agency startup mindset, our engineers don’t just advise; they actively code, debug, and co-build bespoke agentic workflows directly alongside our customers. In this role, you will resolve complex integration, data sovereignty, and security issues within strict compliance frameworks, utilizing talent with TS/SCI clearances. Ultimately, the GPS FDE team accelerates the safe, reliable adoption of generative AI across mission-critical operations while feeding field insights directly back to Google Cloud Product engineering.Google Public Sector brings the magic of Google to the mission of government and education with solutions purpose-built for enterprises. We focus on helping United States public sector institutions accelerate their digital transformations, and we continue to make significant investments and grow our team to meet the complex needs of local, state and federal government and educational institutions.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $207000 - $300000 (USD) + 20% bonus target + equity + benefitsLearn more about benefits at Google.Bachelor's degree in Computer Science, Engineering, a related field, or equivalent practical experience.8 years of experience building and shipping production-grade AI-driven solutions to external or internal customers using Python, TypeScript or comparable languages.Experience building scalable pipelines for structured, unstructured data, incorporating vector databases and RAG-like architectures to power enterprise-grade AI solutions.Experience leading technical discovery sessions with executive stakeholders (C-suite) and engineering teams to define AI and hardware infrastructure requirements.Experience architecting scalable AI systems on cloud platforms.