The Google Public Sector Forward Deployed Engineering (GPS FDE) team 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. We resolve complex integration, data sovereignty, and security issues within strict compliance frameworks. 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.
As a Forward Deployed Engineer (FDE) in Google Public Sector (GPS), you will be an embedded builder who bridges the gap between frontier AI products and production-grade reality within customers. Unlike traditional advisory roles, you will function as a moving beyond high-level architecture to code, debug, and jointly ship bespoke agentic solutions directly within the customer’s environment.
This role is designed for high-agency engineers with a founder’s mindset. You will manage blockers to production including solving the integration complexities, data readiness issues, and state-management issues that prevent AI from reaching enterprise-grade maturity. By embedding with accounts, you serve a dual purpose: providing "white glove" deployment of complex AI systems and acting as a critical feedback loop, transforming real-world field insights into Google Cloud’s future product roadmap.
This role will be focusing on the State and Local Government Market.
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.
US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- 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 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 architecting scalable AI systems on cloud platforms.
- Experience leading technical discovery sessions with executive stakeholders (C-suite) and engineering teams to define AI and hardware infrastructure requirements.
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 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.