Senior Principal Product Manager (Gen AI Platforms)

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- 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

Ability to influence senior leaders across technical and business organizations

Working knowledge of distributed systems, cloud architecture, APIs, and enterprise platforms

Ability to make progress in areas where requirements, technology, or business needs are still evolving

Experience evaluating model choices and understanding the tradeoffs among quality, speed, cost, security, and operational complexity

Sound judgment when evaluating new technologies and determining their practical value

Experience working across functions and geographies in a large organization

Experience leading build, buy, and partner decisions for AI, machine learning, data, or enterprise technology capabilities

Significant experience with generative AI, cloud platforms, enterprise data architecture, or AI powered products

Strong written and verbal communication skills, with the ability to explain complex topics clearly to different audiences

Strong understanding of machine learning pipelines, model deployment, model serving, evaluation, and feedback processes

10 or more years of experience in Product Management, Engineering, Data Science, or a related field

Experience designing shared platform capabilities that can support multiple products, teams, or business functions

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