Senior Principal Product Manager - Gen AI Platforms

Job Summary

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.

Responsibilities

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

Qualifications

• 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

Preferred Qualifications

• Master’s degree or doctorate in Computer Science, Data Science, Statistics, Engineering, Physics, or a related field

• Experience working with models and platforms from OpenAI, Anthropic, Google, or the open source community

• Experience with machine learning operations, model evaluation, AI monitoring, or model observability tools

• Experience defining service requirements for AI systems, including availability, performance, monitoring, usage limits, and operational support

• Knowledge of responsible AI practices, including explainability, model documentation, evaluation, audit processes, governance, and policy implementation

• Experience working with research, engineering, or innovation teams to bring AI capabilities into production

• Experience designing AI user experiences that support transparency, consent, user review, and appropriate human control

• Experience in data centers, cloud infrastructure, telecommunications, or enterprise technology

• Experience building AI assistants, agents, conversational products, or AI enabled workflows

• Experience with business software, enterprise platforms, or products designed for developers