Software Engineering Manager, Applied AI Industry Verticals

About the job:
Like Google's own ambitions, the work of a Software Engineer goes beyond just Search. Software Engineering Managers have not only the technical expertise to take on and provide technical leadership to major projects, but also manage a team of Engineers. You not only optimize your own code but make sure Engineers are able to optimize theirs. As a Software Engineering Manager you manage your project goals, contribute to product strategy and help develop your team. Teams work all across the company, in areas such as information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on and is growing every day. Operating with scale and speed, our exceptional software engineers are just getting started -- and as a manager, you guide the way.
With technical and leadership expertise, you manage engineers across multiple teams and locations, a large product budget and oversee the deployment of large-scale projects across multiple sites internationally.

Responsibilities:
Lead, mentor, and grow a high-performing engineering team of software engineers, fostering an innovative, and highly collaborative engineering culture.
Architect, build, and deliver scalable, maintainable, and secure AI-driven assets and workflows, including domain-specific AI skills, custom connectors, autonomous agents, and Agent-to-User Interfaces (A2UI).
Leverage Google Cloud, enterprise-grade Gemini models, the Gemini connector framework, and Model Context Protocol (MCP) to seamlessly connect Large Language Models (LLMs) to enterprise systems of record.
Design, guide, and vet system designs. Maintain high engineering standards by ensuring code quality, thorough testing, system reliability, and seamless scalability in production.
Partner closely with product management, UX, Forward Deployed Engineer (FDE) team, product team, research teams, and external enterprise partners to translate complex industry requirements into robust AI workflows and concrete technical roadmaps.

Minimum qualifications:
Bachelor's degree or equivalent practical experience.
8 years of experience in software development.
5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
3 years of experience in a technical leadership role.
2 years of experience with generative AI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with generative AI-related concepts (language modeling, computer vision).
2 years of experience in a people management or team leadership role.

Preferred qualifications:
Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
Experience building AI-native front-ends or Agent-to-User interfaces (A2UI), as well as integrating systems using API-centric frameworks and open protocols like Model Context Protocol (MCP).
Experience building software solutions tailored to specific industry verticals.
Experience building applications utilizing Large Language Models (LLMs), prompt engineering, retrieval-augmented generation (RAG) pipelines, or autonomous agent architectures.
Strong track record of designing, scaling, and maintaining highly reliable, highly available production systems with rigorous evaluation and testing frameworks.