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.
About the job
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, 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.
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.
US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
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.
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 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.
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