Job description

The Google Public Sector Forward Deployed Engineering (GPS FDE) team is a squad of direct "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 challenges 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. 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 + benefits Learn more about benefits at Google . • Serve as a developer for complex AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, model context protocol (MCP) servers) that drive measurable return on investment. • 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 rigorous 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. • Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience. • 8 years of experience with software development using Python or similar coding languages. • Experience architecting AI systems on cloud platforms (e.g. Google Cloud Platform (GCP)). • Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions. • Experience leading technical discovery sessions with customers. Minimum qualifications: • Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience. • 8 years of experience with software development using Python or similar coding languages. • Experience architecting AI systems on cloud platforms (e.g. Google Cloud Platform (GCP)). • Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions. • Experience leading technical discovery sessions with customers. 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, ADK) and complex patterns (e.g., ReAct, self-reflection, hierarchical delegation). • Proficiency in Vertex AI Pipelines, Kubeflow, or MLflow to implement robust CI/CD/CT automation and experimentation. • Experience architecting integrated systems, navigating real-time inference constraints, and implementing model quantization for resource-constrained environments. • Experience designing resilient data engineering pipelines using BigQuery and VertexAI for enterprise-scale analytics. • Knowledge of Large Language Model (LLM) native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.

Requirements

Must-haves

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
Learn more about benefits at Google .
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 rigorous 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.
Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
8 years of experience with software development using Python or similar coding languages.
Experience architecting AI systems on cloud platforms (e.g. Google Cloud Platform (GCP)).
Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions.
Experience leading technical discovery sessions with customers.
Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
8 years of experience with software development using Python or similar coding languages.
Experience architecting AI systems on cloud platforms (e.g. Google Cloud Platform (GCP)).
Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions.
Experience leading technical discovery sessions with customers.

Nice-to-haves

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
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, ADK) and complex patterns (e.g., ReAct, self-reflection, hierarchical delegation).
Proficiency in Vertex AI Pipelines, Kubeflow, or MLflow to implement robust CI/CD/CT automation and experimentation.
Experience architecting integrated systems, navigating real-time inference constraints, and implementing model quantization for resource-constrained environments.
Experience designing resilient data engineering pipelines using BigQuery and VertexAI for enterprise-scale analytics.
Knowledge of Large Language Model (LLM) native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.

Application prep

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Keywords to mirror

aipythongcpreactci/cdllm

Platform: google
Effort: medium