Senior AI Product Operations Manager, Growth AI Transformation

Google•New York, NY

12h•$176,000 - $256,000

About The Position

The Growth AI Transformation team is the central marketing team driving Owned and Operated (O&O) growth agentic innovation across Google Marketing. Its mission is to reduce the time between strategy and execution, shifting from output-volume metrics toward agentic impact, velocity, and value realization. In this role, described in the posting as an AI Marketing Solutions Architect, you will scope and deliver key O&O agentic workflows end to end, helping develop autonomous growth beyond traditional campaign management. You will advocate long-term product strategy across multiple teams and help ensure intelligent systems can scale globally. The work combines technology and user-centered marketing to demonstrate how Google products solve user problems.

Requirements

Bachelor's degree or equivalent practical experience.
11 years of experience in management consulting, product management and strategy, or analytics in a technology company.
Experience working with and analyzing data, and managing multiple cross-functional programs or projects.
Experience working with Generative AI Agent Capabilities and LLM/Model Orchestration.
Experience with User Experience (UX) Strategy and Information Architecture.

Nice To Haves

Experience integrating LLMs or agentic tools into production-ready web environments or growth-focused workflows, such as autonomous journey orchestration or algorithmic brief generation.
Expertise in designing MarTech architectures or AI-driven growth applications.
Ability to analyze situations at a system level and understand how technical and organizational components interrelate over time within agentic ecosystems.
Ability to build and test code quickly to solve targeted problems while maintaining clean, reusable structures for future patterns.
Skill in using analytics to diagnose root causes of agent failure and optimize performance.

Responsibilities

Design and structure prompt-aware content so AI agents provide accurate, empathetic responses, and determine when deterministic context versus LLM reasoning is needed.
Organize, tag with metadata, and chunk internal data to optimize Retrieval-Augmented Generation (RAG) and semantic search systems.
Analyze failed agent runs and awkward handoffs, identify context gaps, and implement real-time fixes to the agent knowledge base.
Partner with Product, Legal, and Brand teams to gather and translate functional knowledge into structured formats that AI agents can use.
Define durable, long-term user experience strategies across multiple teams, drive cross-functional alignment on complex or open-ended challenges, and mitigate unintentional harm in agentic ecosystems.