Senior Principal Product Manager, Equinix

• 10 or more years of experience in Product Management, Engineering, Data Science, or a related field
• Significant experience with generative AI, cloud platforms, enterprise data architecture, or AI powered products
• Demonstrated ability to operate as a senior individual contributor across both technical and business topics
• Experience working directly with large language models, prompt design, retrieval augmented generation, model customization, or fine tuning
• Experience evaluating model choices and understanding the tradeoffs among quality, speed, cost, security, and operational complexity
• Experience leading build, buy, and partner decisions for AI, machine learning, data, or enterprise technology capabilities
• Strong understanding of machine learning pipelines, model deployment, model serving, evaluation, and feedback processes
• Working knowledge of distributed systems, cloud architecture, APIs, and enterprise platforms
• Experience designing shared platform capabilities that can support multiple products, teams, or business functions
• Ability to influence senior leaders across technical and business organizations
• Strong written and verbal communication skills, with the ability to explain complex topics clearly to different audiences
• Sound judgment when evaluating new technologies and determining their practical value
• Experience working across functions and geographies in a large organization
• Ability to make progress in areas where requirements, technology, or business needs are still evolving

Desirable

• Designs, develops and manages the lifecycle of a product or group of products from concept to launch to end of life. Translates market opportunities and customer demand into viable products and services that differentiate Equinix in the market
• Sets the vision and strategy for their product ensuring it is competitively positioned and customer-centric. Manages the product roadmap including features, upgrades and maintenance of the product or product line
• Works cross functionally with user experience, engineering, operations, solution architects, marketing and others to design, build and launch new products and/or product features
• Generative AI, Cloud, and Data Architecture:
• Bring strong working knowledge of generative AI, cloud infrastructure, and enterprise data architecture to product and platform decisions
• Partner with engineering and architecture teams to evaluate large language model architectures, AI agents, cloud deployment approaches, and enterprise data pipelines
• Participate meaningfully in technical and architecture reviews, as well as product and roadmap discussions
• Translate technical choices, constraints, and risks into clear business implications that leaders can understand and act on
• Help ensure that AI products are designed for enterprise scale, security, reliability, and reuse
• Build, Buy, and Partner Decisions:
• Establish a consistent approach for determining when Equinix should build AI capabilities internally, purchase commercial technology, or partner with external providers
• Evaluate cloud AI services, commercial model providers, open source technologies, and enterprise AI platforms
• Assess options based on business value, implementation time, cost, technical fit, security, operational complexity, and long term strategic importance
• Develop clear recommendations supported by financial analysis, technical assessment, and risk considerations
• Present recommendations to senior leaders and support informed investment decisions
• Model Strategy and Deployment:
• Guide decisions on prompt design, retrieval augmented generation, model customization, fine tuning, and model selection
• Help teams determine when a smaller model may provide better performance, cost, speed, or control than a larger model
• Partner with AI and machine learning engineering teams on deployment approaches across cloud, private infrastructure, and environments with strict performance requirements
• Evaluate emerging approaches such as AI agent coordination, model routing, and hybrid model deployment
• Use model performance data, evaluation results, user feedback, and business outcomes to guide product priorities
• Platform Reliability and Responsible AI:
• Define product requirements for AI system reliability, availability, performance, monitoring, usage limits, and incident response
• Partner with engineering teams to improve visibility into AI system behavior, model performance, cost, and production issues
• Establish requirements that support traceability, explainability, auditability, fairness, privacy, and regulatory compliance
• Work with Legal, Security, Privacy, and AI Governance teams to incorporate company policies and responsible AI requirements into products and platforms
• Partner with Design to create clear user experiences that explain how AI is being used and provide appropriate user review, control, and approval
• Product Decisions and Risk Management:
• Lead decisions involving tradeoffs among business value, delivery speed, technical complexity, cost, performance, and risk
• Establish clear and repeatable methods for evaluating major AI product and architecture decisions
• Help teams identify risks early and determine the appropriate level of governance and human oversight
• Balance the need to deliver value quickly with the requirements of security, reliability, and responsible AI
• Executive and Cross Functional Leadership:
• Build alignment across Product, Engineering, Architecture, Legal, Information Security, Data, Design, and business functions
• Influence decisions through expertise, clear reasoning, and strong working relationships rather than direct authority
• Explain complex technical topics in clear language that is relevant to both technical and business audiences
• Work with senior leaders to connect AI investments to business priorities, customer value, productivity, and operational outcomes
• Develop trusted relationships so that teams engage early when evaluating important AI opportunities or decisions
• Industry and Technology Assessment:
• Stay current on developments in generative AI, including models, platforms, tools, enterprise applications, and deployment methods
• Evaluate which technologies are relevant to Equinix and which are unlikely to provide meaningful business value
• Develop informed perspectives that help guide product strategy, architecture, partnerships, and investment
• Represent Equinix’s perspective on generative AI internally and, where appropriate, with customers, partners, and the broader industry