Senior Director of Generative AI Product Management, Equinix

• Analytical problem solver with the ability to work under pressure and multitask while leading cross-functional teams
• Excellent communicator able to simplify the complex
• Ability to build and maintain strong business relationships with key stakeholders as a trusted advisor
• Ability to demonstrate ownership and proactively drive initiatives
• Ability to balance collaboration and driving alignment, and being decisive
• Minimum of 8 years’ experience in Product Management and/or Data Science, preferably within the data/analytics domain, either internal facing or both internal and customer facing products (e.g., commercialized and generating revenue)
• Track record of leading successful AI/ML or Generative AI product related programs​from project initiation through development and implementation, with quantified success metrics
• Strong understanding of LLM concepts (prompting, RAG, embeddings, fine-tuning, agents)
• Proven ability to lead cross-functional teams and ship products at scale
• Excellent communication and interpersonal skills, with proven track record of building strong relationships with business stakeholders
• Experience operating in a highly cross functional, multinational business
• Dynamic, forward-thinking leader with a bias for action, rapid experimentation, and ability to break down complex problems

Desirable

• We’re looking for a Senior Director of Generative AI Product Management to own the strategy, development, and delivery of Generative AI–powered products
• This role sits at the intersection of product vision, cutting-edge AI technology, and real customer value
• You’ll translate fast-moving GenAI capabilities into scalable, safe, and impactful products
• This role is responsible for driving Equinix’s applied Generative AI strategy, operate as an embedded Gen AI expert across Go to Market, Operations, Product, and Corporate domains, advising, partnering, or leading initiatives based on strategic priority and readiness
• Define and own the GenAI product vision, roadmap, and success metrics in alignment with strategic business goals, and short and long-term priorities
• Serve as the SME to work across domain on how Equinix embed Gen AI in internal analytics, workflows, and customer facing process to drive revenue and efficiency impact
• Communicate to Enterprise employees and Executives on GenAI tools and features are enabled
• Decide what to build as reusable enterprise capability (e.g., a central RAG infrastructure, prompt management layer) vs. bespoke domain solutions
• Stay ahead of GenAI trends (LLMs, multimodal models, agents, RAG, fine-tuning, evaluation frameworks) and drive innovation in the company
• Own Enterprise GenAI strategy and execution for Equinix ‘Sidekick’ and build a Platform vision for reusable components of Sidekick
• Define vision of RAG-AS-Service and Agents-as-Service
• Define vision of how Sidekick can coexist with SAAS Native GenAI agents along with few internal high value Agents
• A Startup mindset of building Sidekick as a Product for solving diverse domains in Enterprise
• Proactively mine the organization for high-value Gen AI opportunities
• Build a Training academy of the Sidekick platform and LLM/LLR fundamentals to accelerate Sidekick adoption
• Distinguish between "cool demos" and production-worthy investments
• Build a scoring framework: business impact × feasibility × data readiness × risk
• Work with Business Operations and finance counter parts to enable prioritization across use cases
• Lead end-to-end product development from discovery to launch
• Drive actual usage, not just deployment
• Build training, documentation, and change management into every launch
• Drive rapid experimentation, MVPs, and iterative improvements
• Work with ML/AI teams on model selection, prompting strategies, fine-tuning, and evaluation
• Make trade-offs between latency, cost, accuracy, and safety
• Ensure responsible AI practices (privacy, bias, explainability, compliance)
• Partner with Data Governance leader to define guardrails: what data can flow to which models, what use cases are off-limits
• Partner with Legal, Security, and Compliance on policy
• Own the responsible AI framework: bias testing, hallucination monitoring, human-in-the-loop requirements