Senior Staff Software Engineer, Data Infrastructure AI Agent

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.

Google Cloud Business Platform (GCBP) drives 10X scale for Google Cloud by accelerating growth, improving reliability via a Unified Data Layer, and increasing efficiency. Within GCBP, the Cloud Data Analytics and Intelligence (CDANI) team powers the GCP Enterprise's agentic transformation by unifying data, access, and GenAI layers into secure, daily-habit interactions.

In this role, you will bridge Google Cloud's core data ecosystem and agentic applications. You will drive the technical strategy, architecture, and implementation of foundational GenAI capabilities across the GCP Enterprise enabling the rapid deployment of secure, high-performance, and personalized AI agents and digital coworkers.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $262000 - $364000 (USD) + 25% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Own the technical strategy and architecture for AI-ready data foundations, including high-throughput processing pipelines, unified storage, and enterprise semantic models.
• Architect horizontal search and summarization agents, alongside modular frameworks and personalization capabilities that allow teams to safely onboard business logic.
• Drive systemic latency optimizations across the agent execution stack to meet strict end-to-end P50 goals for real-time queries.
• Design automated evaluation frameworks to improve agent accuracy. Establish data governance, security, and compliance primitives to power real-time analytics.
• Lead the Data and AI engineering track. Guide designs, mentor developers, and collaborate cross-functionally to integrate identity management and access controls.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
  
• 8 years of experience programming in Java and Python.
• 5 years of experience with design and architecture; and testing/launching software products.
• Experience architecting, designing, and developing large-scale distributed systems.
• Experience designing or deploying distributed data processing systems (e.g., BigQuery, Cassandra, PostgreSQL).
• Experience designing, developing, and deploying generative AI agents or large language model (LLM) applications.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  
• 5 years of experience in a technical leadership role leading project teams and setting technical direction.
  
• 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
• Experience with semantic data modeling and integrating generative AI agents with external APIs, databases, or enterprise systems.

In most instances, this position requires in-person interviews as part of the hiring process.