Technical Program Manager, Server Operations AI
Minimum qualifications:
- Bachelor's degree in a technical field, or equivalent practical experience.
- 8 years of experience in program management.
- Experience working on software automation for data centers and presenting data to audiences.
- Experience in AI algorithms, data collection methodologies, and data analysis.
Preferred qualifications:
- 8 years of experience managing cross-functional or cross-team projects.
- Experience in developing and tracking project timelines and deliverables.
- Experience with data visualization.
- Experience making complex messages land within a large organization with excellent communication strategy skills.
- Ability to manage projects from initiation to completion.
About the job
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 mission of the AI Strategy and Software Enablement (AiS2E) team is to serve as the centralized strategic engine for all Google Server Operations (GSO) software and AI initiatives. By prioritizing software development, defining predictive data platforms, scaling bottom-up field innovations, and future-proofing new infrastructure, AiS2E empowers Google Server Operations to reliably achieve its scaling goals.
Responsibilities
- Guide stakeholder management to identify, engage, and manage relationships with key stakeholders and Subject Matter Experts (SMEs).
- Develop and maintain project plans, timelines, and tracking mechanisms.
- Manage the model lifecycle from identifying data sources and building processing workflows to validating, refining, and incorporating stakeholder feedback for continuous improvement.
- Socialize data-driven insights with key stakeholders and track their influence on strategic business decisions and project outcomes.
- Manage stakeholder engagement by providing regular sprint updates and effectively presenting final project outcomes and findings.