Forward Deployed Engineer, State and Local Government, Google Public Sector

Google•New York, NY

6d•$207,000 - $300,000

About The Position

The Forward Deployed Engineer will work on the Google Public Sector Forward Deployed Engineering team, focusing on the State and Local Government market. The team rapidly deploys secure, production-grade AI solutions across Federal and SLED environments. Engineers work directly alongside customers to code, debug, and co-build bespoke agentic workflows, addressing integration, data readiness, data sovereignty, state-management, and security challenges within strict compliance frameworks. This is an embedded, high-agency engineering role that bridges Google’s AI products and customer production environments. The engineer will help move solutions from prototypes to reliable enterprise-grade systems, provide hands-on deployment support, and relay field insights to Google Cloud Product Engineering to inform future products and the product roadmap. Individual pay is determined by job-related skills, experience, and relevant education or training. The stated US compensation range is $207,000–$300,000, plus a 20% bonus target, equity, and benefits.

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 architecting scalable AI systems on cloud platforms.
Experience leading technical discovery sessions with C-suite stakeholders and engineering teams to define AI and hardware infrastructure requirements.

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, working with real-time inference constraints, and implementing model quantization for resource-constrained environments.
Proficiency with Vertex AI Pipelines, Kubeflow, or MLflow for CI/CD/CT automation and experimentation.
Knowledge of LLM-native metrics, including tokens per second and cost per request, and techniques for optimizing state management and granular tracing.
Experience designing resilient data engineering pipelines using BigQuery and Vertex AI for enterprise-scale analytics.

Responsibilities

Develop complex AI applications, moving rapid prototypes into production-grade agentic workflows, such as multi-agent systems and MCP servers, that deliver measurable ROI.
Architect and code integrations between Google AI products and customers’ live infrastructure, including APIs, legacy data silos, and security perimeters.
Build high-performance evaluation pipelines and observability frameworks to assess agentic systems for accuracy, safety, and latency.
Identify recurring 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 practices to support long-term project success and end-user adoption.
Work directly with customers to resolve integration, data readiness, data sovereignty, security, and state-management challenges affecting production deployment.
Provide field insights to Google Cloud Product Engineering to inform product development and the product roadmap.