Senior Product Manager at Equinix — NeverHard

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 Product Lifecycle Management Executes on the defined strategy and vision for AI within the Quoting, Solutioning, and Contracting domains of Lead to Cash Manages the full AI product lifecycle — from ideation and discovery through build, launch, adoption, and continuous improvement Maintains awareness of the competitive AI landscape and emerging technologies applicable to CPQ, CLM, and solution design workflows Product Strategy, Vision and Roadmap Partners with Sales, Tech Sales, and Sales-supporting teams to translate business needs into clearly scoped, AI-enabled solutions Gathers, documents, and designs the best possible end-user experience across quoting, solutioning, and contracting workflows — incorporating the voice of the customer and the voice of the rep into the AI product roadmap Involves engineers, designers, data scientists, and business stakeholders to create a shared vision and clear, measurable goals for each AI product initiative Identifies and documents where AI reduces friction across the quote-to-contract lifecycle — pricing inconsistency, manual contract redlines, slow approvals, scope estimation errors — and incorporates those opportunities into a prioritized AI roadmap Defines a multi-horizon roadmap that balances quick-win automation with longer-term intelligent decision support across all three domains: Product Performance Defines the metrics and KPIs used to measure AI-enabled product success across all three domains Establishes baselines and targets for each AI initiative prior to launch Monitors model performance post-launch, defines retraining triggers, and leads product retrospectives tied to outcome data Produces executive-level reporting on AI initiative ROI, adoption, and delivery progress Cross-Functional Collaboration Designs product training curriculum for AI features across Quoting, Solutioning, and Contracting — ensuring end users understand not just how to use AI tools, but when to trust, override, or escalate AI outputs Leads cross-functional trainings, demos, and working sessions with users and business stakeholders Partners with data engineering on feature pipelines, training datasets, data quality standards, and model retraining workflows Backlog Prioritization Creates and owns the AI product backlog across Quoting, Solutioning, and Contracting — writing clear user stories, acceptance criteria, and definition of done for every AI feature Works regularly with the team to refine the backlog, add detail, resolve dependencies, and sequence work by business value and technical feasibility Collaborates closely with engineering and data science to size work, surface risks, and maintain a healthy sprint-ready backlog Applies Lean principles to eliminate process waste before layering AI on top — ensuring AI solves real friction, not just digitizes broken workflows Manages dependencies between AI feature teams and platform, integration, and infrastructure teams Test Case Definition and UAT Coordination Leads definition of allowable configurations, test cases and executes UAT Stakeholder Management Manages stakeholder expectations within and/or across functions Identifies and proactively includes correct stakeholders and communications effectively Understands stakeholder needs and builds effective relationships Utilizes effective methods of communication with stakeholders, varying approach accordingly