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 building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions.
- Experience architecting AI systems on cloud platforms (e.g., Google Cloud Platform (GCP)).
- Experience leading technical discovery sessions with customers.
- Must possess an active Top Secret/SCI security clearance.
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).
- Proven experience architecting integrated systems, navigating real-time inference constraints, and implementing model quantization for resource-constrained environments.
- Knowledge of Large Language Model (LLM) native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
- Proficiency in Vertex AI Pipelines, Kubeflow, or MLflow to implement robust CI/CD/CT automation and experimentation.
- Designing resilient data engineering pipelines using BigQuery and VertexAI for enterprise-scale analytics.
About the jobThe 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.
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 .
Responsibilities- 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.
