Who you are

- 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

- 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

What the job involves

- 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 explicit 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

📌 Senior Principal Product Manager (Toronto)
🏢 Equinix
📍 Toronto