Principal Product Manager - Conversational AI & Agent Platform

Job Summary

This is a senior individual contributor role with broad influence across the company. You will own the product strategy for Equinix's conversational AI and assistant platforms — chat, voice, and agentic interfaces that employees, customers, and partners interact with directly. You will work closely with engineering and architecture teams on technical decisions while also helping executives understand business implications, investment choices, and risk.

You will serve as a conversational AI product leader across Go to Market, Operations, Product, and Corporate functions. Depending on the initiative, you may advise business leaders, partner with product and engineering teams, or directly lead product strategy and delivery for conversational experiences and the platform capabilities that power them.

The conversational AI market is changing quickly — from single-turn chatbots to multi-agent, tool-using assistants. An important part of this role is evaluating new conversational AI technologies, identifying which capabilities are relevant to Equinix, and translating them into practical product and platform investment decisions.

Responsibilities

Conversational AI, Cloud, and Data Architecture

• Bring strong working knowledge of conversational AI, dialogue systems, agent orchestration, cloud infrastructure, and enterprise data architecture to product and platform decisions.

• Partner with engineering and architecture teams to evaluate large language model architectures, conversational agent design patterns (orchestrator/worker, multi-agent handoff, tool calling), cloud deployment approaches, and enterprise data pipelines.

• Participate meaningfully in technical and architecture reviews, as well as product and roadmap discussions for the conversational platform.

• Translate technical choices, constraints, and risks into clear business implications that leaders can understand and act on.

• Help ensure conversational AI products are designed for enterprise scale, security, reliability, multi-channel support (chat, voice, embedded assistants), and reuse across business functions.

Conversation Design, Model Strategy, and Deployment

• Guide decisions on prompt design, retrieval augmented generation, conversation flow and dialogue management, model customization, fine tuning, and model selection for conversational use cases.

• Help teams determine when a smaller model may provide better latency, cost, or control for real-time conversational interactions than a larger model.

• Partner with AI and machine learning engineering teams on deployment approaches for conversational agents across cloud, private infrastructure, and low-latency environments.

• Evaluate emerging approaches such as multi-agent coordination, model routing, tool/function calling, memory and context management, and hybrid model deployment for conversational systems.

• Use conversation quality metrics, containment and escalation rates, model performance data, evaluation results, user feedback, and business outcomes to guide product priorities.

Platform Reliability and Responsible AI

• Define product requirements for conversational AI system reliability, availability, latency, performance, monitoring, usage limits, and incident response.

• Partner with engineering teams to improve visibility into conversational agent behavior, model performance, cost, conversation logs, and production issues.

• Establish requirements that support traceability, explainability, auditability, fairness, privacy, and regulatory compliance across conversational interactions.

• Work with Legal, Security, Privacy, and AI Governance teams to incorporate company policies and responsible AI requirements into conversational products and platforms, including data handling within chat, voice, and memory features.

• Partner with Design to create clear conversational UX that explains how AI is being used, manages user expectations, and provides appropriate review, control, hand-off to a human, and approval within the conversation itself.

Product Decisions and Risk Management

• Lead decisions involving tradeoffs among business value, delivery speed, technical complexity, cost, performance, and risk for conversational AI features.

• Establish clear and repeatable methods for evaluating major conversational AI product and architecture decisions.

• Help teams identify risks early — including hallucination, unintended actions, and improper data exposure in conversational contexts — and determine the appropriate level of governance and human oversight.

• Balance the need to deliver conversational 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 on the conversational AI roadmap.

• 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 conversational AI investments to business priorities, customer value, productivity, and operational outcomes.

• Develop trusted relationships so that teams engage early when evaluating important conversational AI opportunities or decisions.

Industry and Technology Assessment

• Stay current on developments in conversational AI, including foundation models, agent frameworks, orchestration patterns, voice interfaces, enterprise assistant platforms, and deployment methods.

• Evaluate which conversational AI 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 for the conversational platform.

• Represent Equinix's perspective on conversational 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 conversational AI, 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, agentic/multi-agent systems, model customization, or fine tuning

• Experience evaluating model and platform choices and understanding the tradeoffs among quality, latency, cost, security, and operational complexity for real-time conversational use cases

• Experience leading build, buy, and partner decisions for conversational 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 (e.g., orchestration layers, shared agent frameworks, conversational memory) 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 conversational AI 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, including agent frameworks and orchestration tooling

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

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

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

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

• Experience designing conversational AI user experiences that support transparency, consent, user review, and appropriate human control (e.g., escalation to a live agent)

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

• Experience building AI assistants, chatbots, voice agents, or AI-enabled conversational workflows at enterprise scale

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