Software Engineer, Rapid Innovation, Google Public Sector

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward. As a Software Engineer, you will support the Rapid Innovation team in Google Public Sector. You will construct rapid prototype generative AI applications tailored to public sector customers. You will leverage generative AI technologies to develop solutions and validate their efficacy. You will swiftly showcase the latest generative AI capabilities through direct collaboration with customers. In this role, you will closely collaborate with our Product team to eliminate obstacles and shape the future trajectory of our offerings. In addition, you will disseminate the lessons learned to customers and internal Google teams. 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: $174000 - $252000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google . Act as a trusted advisor to customers by understanding their business process and objectives. Design and build genAI-driven solutions spanning AI, data, and infrastructure. Demonstrate how Google Cloud is differentiated by working with customers on application prototypes; demonstrating generative AI features; prompting and tuning models; and optimizing model performance, profiling, and benchmarking. Troubleshoot and find solutions to issues in generative AI applications. Build repeatable technical assets such as scripts, templates, reference architectures, etc. to enable customers and internal teams. Work with peers to include the full cloud stack into overall architecture. Work cross-functionally to influence Google Cloud strategy and product direction at the intersection of infrastructure and AI/ML by advocating for enterprise customer requirements. Coordinate regional field enablement with leadership and work closely with product and partner organizations on external enablement. Travel as needed. Minimum qualifications: Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience. 5 years of experience with software development using Python or similar coding languages. Experience architecting AI systems on cloud platforms (e.g., GCP). Experience building pipelines for structured and unstructured data using both vector databases and (retrieval-augmented generation) RAG-like architectures to power enterprise AI solutions. Experience leading technical discovery sessions with customers. Must possess an active Top Secret/SCI security clearance with current polygraph. 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 patterns (e.g., ReAct, self-reflection, hierarchical delegation). Knowledge of "LLM-native" metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.