Software Engineering Manager Ii, AI Productivity

Google Google · Big Tech · São Paulo, State of São Paulo, Brazil

Software Engineering Manager II for Google's AI Productivity team, focusing on internal AI products like go/sqlminer and go/agentflux. The role involves technical leadership, people management, and architecting solutions at the intersection of Generative AI, SQL pipelines, and AI agent evaluations, with a focus on integrating LLM capabilities and evolving agent evaluation pipelines.

What you'd actually do

  1. Collaborate with local Brazilian engineering teams as well as global cross-functional AI Productivity, Developer Platforms, and Safety/Security teams to align on roadmap priorities and deliver end-to-end agentic capabilities.
  2. Establish clear expectations with individuals based on their level and role, aligned to the broader organization's goals, meet regularly to discuss performance and development and provide feedback and coaching.
  3. Maintain a strong presence by building solutions, participating in technical design, and conducting code reviews, while helping the team navigate the non-deterministic nature of LLM systems.
  4. Drive the roadmap for integrating advanced LLM capabilities (e.g., multimodal, agent tools, Simula synthetic data generation) into internal SQL engines, ensuring low-latency and cost-effective execution.
  5. Evolve the automated agent evaluation pipeline by expanding tool profiling and enhancing the automated red-team auditing to ensure mutating operations are safely caught and mocked.

Skills

Required

  • software development
  • technical leadership
  • people management
  • team leadership
  • integrating generative AI tools or Large Language Model (LLM) interfaces into workflows

Nice to have

  • Master's degree or PhD in Computer Science or related technical field
  • working in a complex, matrixed organization
  • building application layers with LLMs
  • prompt engineering
  • agentic platforms (e.g., LangChain, ADK/Orcas)
  • vector search
  • semantic embeddings
  • AI evaluation
  • model quality assurance
  • automated red-teaming
  • software verification frameworks

What the JD emphasized

  • technical leadership
  • people management
  • architect, design, and prototype solutions
  • Generative AI
  • AI agent evaluations
  • integrating advanced LLM capabilities
  • automated agent evaluation pipeline
  • integrating generative AI tools or Large Language Model (LLM) interfaces into workflows
  • AI evaluation
  • model quality assurance
  • automated red-teaming

Other signals

  • leading a highly AI-focused team
  • working on two of Google's flagship internal AI productivity products
  • architecting, designing, and prototyping solutions at the intersection of Generative AI, SQL pipelines, and AI agent evaluations
  • integrating advanced LLM capabilities into internal SQL engines
  • evolving the automated agent evaluation pipeline