Technical Program Manager III, AI Agents Development, Cloud Infrastructure

Google•Pryor Creek, OK

2d•$163,000 - $237,000

About The Position

A problem isn’t truly solved until it’s solved for all. That’s why Googlers build products that help create opportunities for everyone, whether down the street or across the globe. As a Technical Program Manager at Google, you’ll use your technical expertise to lead complex, multi-disciplinary projects from start to finish. You’ll work with stakeholders to plan requirements, identify risks, manage project schedules, and communicate clearly with cross-functional partners across the company. You're equally comfortable explaining your team's analyses and recommendations to executives as you are discussing the technical tradeoffs in product development with engineers. The Central Ops team globally manages programs and standards across the Data Center Operations fleet. The functions provide operational execution support (e.g., Incident Management, Fleet Deployment, Spares, and Maintenance Planning) along with program oversight (e.g., Operational Assessments, Data Quality, Business Process support) and transformation program partnership with the field operations. The team supports new product introductions into the fleet. We are transitioning from static traditional tools to active, multi-agent AI workflows. These agents will autonomously diagnose equipment anomalies, optimize liquid and traditional cooling loops in real-time, orchestrate server swap-out logistics, and automate complex incident resolution paths across our physical and digital fleet. As the AI Agents Development Technical Program Manager, you will lead the end-to-end execution, delivery, and scaling of autonomous, decision-making AI systems designed to optimize global data center operations. In this high-impact, cross-functional role, you will sit at the intersection of physical infrastructure and software engineering. You will partner closely with software developers, systems architects, site operations leads, and hardware engineering teams to build robust, reliable, and production-grade operational software. Behind everything our users see online is the architecture built by the Technical Infrastructure team to keep it running. From developing and maintaining our data centers to building the next generation of Google platforms, we make Google's product portfolio possible. We're proud to be our engineers' engineers and love voiding warranties by taking things apart so we can rebuild them. We keep our networks up and running, ensuring our users have the best and fastest experience possible.

Requirements

Bachelor's degree in a technical field, or equivalent practical experience.
5 years of experience in program management.
Experience driving technical software programs in an engineering organization.
Experience building or deploying AI agents.

Nice To Haves

5 years of experience managing cross-functional or cross-team projects.
Prior experience working within hyperscale data center operations, building automation tools, or interacting with DCIM/CMMS environments.
Track record of launching production-grade AI Agents that execute multi-step reasoning, tool usage, and real-time decision-making.
Excellent capability to navigate extreme ambiguity, drive technical alignment across highly matrixed organizations, and translate complex operational needs into structured engineering backlogs.

Responsibilities

Drive the execution of our DCOps AI Agent roadmap, from initial concept and architecture design through deployment, evaluation, and scaling.
Manage complex, cross-functional dependencies across Software Engineering, Hardware Logistics, Facility Operations, and Security teams to ensure seamless integration of AI agents into active DCOps systems.
Manage release processes for AI Agent deployment, ensuring smooth transitions from development and testing to physical production environments.
Collaborate with system architects to design scalable agentic platforms (including multi-agent coordination, Retrieval-Augmented Generation (RAG) pipelines, and Tool Use/MCP integration).
Identify and resolve systemic bottlenecks, model drift, data pipeline delays, and operational risks associated with deploying autonomous systems in high-stakes environments.