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,