Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 5 years of experience in product management or a related technical role.
• Experience with drivers of quality across the agent system (e.g., model, harness, data, deployment environments).
• Experience in building and deploying AI agents in a regulated sector such as financial services, healthcare, insurance, human resources.

Preferred qualifications:

• Experienced in building and deploying AI agents for medium-to-large businesses and enterprises.
• Familiar with building evaluations for Large Language Models (LLMs) and agents.
• Ability to quickly understand customer needs in new domains, and form and test hypotheses on technical solutions and evaluations to improve model/agent outcomes to meet needs.

About the job

As a Product Manager for Gemini for Enterprise Verticals, you will drive the strategy to make Gemini models the market leader across key industries. You will serve as the crucial link between our enterprise customers and AI research teams, deeply understanding customer needs and translating them into a model improvement strategy and roadmap that delivers real-world value. This role requires a unique blend of technical AI expertise, strategic thinking, and a relentless focus on the customer experience.

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.

Responsibilities

• Become a trusted advisor to our customers, deeply understanding their challenges and opportunities to inform the product strategy for improving our models and agentic capabilities.
• Translate customer insights into a compelling product vision and a concrete roadmap, partnering with research and engineering teams to execute and iterate.
• Guide critical technical decisions on model architecture, training, and evaluation frameworks, always with the customer's end goal in mind.
• Engage directly with customers to drive adoption and gather feedback, ensuring our models are not just technically excellent, but also solve real-world problems.
• Convert ambiguity and complex problems into clear, actionable plans for the team.