About the Role
As the Product Manager for the Global Platform Engineering (GPE) team within the Data & AI/ML (DAI) portfolio at Priceline, you will be instrumental in defining the strategy and ensuring the delivery of foundational platforms that power AI, ML, and data-driven product experiences across the company. This role focuses on shaping how AI is utilized internally, with your primary customers being other PMs, engineers, and stakeholders across various portfolio teams. Success will be measured by the adoption rate of your platforms and the acceleration and quality they bring to the business. You will collaborate with a distributed team of platform engineers and architects and report directly to the Director of Product for the DAI Portfolio. This position offers high visibility, C-suite buy-in, and leadership support to drive significant product innovation.
Responsibilities
Product Discovery & Strategy
- Deeply understand internal customer needs through user research, data analysis, feedback loops, and partnerships with other PMs.
- Define the product vision, goals, and outcomes in alignment with the portfolio strategy, including foundational capabilities such as:
- Centralized LLM Management: Platformize a centralized LLM gateway, including tiered onboarding, key governance, and cost/usage tracking.
- AI Quality & Reliability: Increase org-wide adoption of evals, guardrails, and observability tooling for measurable AI-powered feature quality.
- MCP Gateway & Governance: Drive vendor evaluation, architectural decisions, and governance for emerging agentic infrastructure.
- ML Pipelines: Own the migration and modernization of ML use cases onto a new platform and framework.
- AI-Powered Personalization: Partner with other portfolios to build tools and platforms enabling AI-driven personalization (e.g., personalized sort and ranking) across the Priceline user experience.
Outcome Ownership
- Define, track, and deliver against product OKRs, KPIs, and success metrics.
- Treat platform adoption across portfolios as a first-class outcome.
- Continuously evaluate whether product initiatives and platform investments drive intended value.
Backlog Management & Prioritization
- Own the GPE product backlog, ensuring work is clearly defined, prioritized, and sequenced.
- Write clear product requirements, user stories, and acceptance criteria.
- Partner with engineering and product leads to assess feasibility, estimate effort, and refine scope.
- Make explicit prioritization trade-offs across AI-first initiatives, classical ML, technical debt, and data infrastructure investments.
Planning & Execution
- Lead quarterly and sprint planning for the GPE product team, driving disciplined delivery.
- Track progress, surface scope changes early, and keep the team focused on high-priority work.
- Participate in demos, standups, retrospectives, and other sprint rituals.
- Identify and manage cross-team dependencies, especially with portfolio teams onboarding to your platforms.
Vendor & Cost Management
- Partner with architecture and portfolio leadership to lead vendor selection and ongoing management for the AI/ML stack tooling.
- Build and maintain cost and usage observability for centralized AI infrastructure, defining rules of engagement for consumption while maintaining accountability for spend.
Stakeholder Alignment & Adoption
- Communicate product vision and progress, aligning with business sponsors and cross-functional teams.
- Convey the value of foundational platform work to non-technical stakeholders by connecting infrastructure investment to tangible business outcomes.
- Drive measurable adoption of GPE tools and platforms through clear onboarding paths, self-service documentation, and office hours.
Requirements
- At least 3-4 years of Product Management experience, with ownership over product vision, priorities, and impact delivery.
- Direct experience building products with AI-powered functionality, understanding both the AI product lifecycle and the operational/technical realities of running LLMs in production.
- Strong technical foundation from prior engineering, data, analytics, or platform PM experience; comfortable with deep technical concepts and conversations.
- Familiarity with modern AI/ML and data infrastructure (e.g., classical MLOps, GenAI infrastructure, GCP) is a plus.
- Contribution to vendor evaluations for technical products and comfort navigating strategic build vs. buy decisions.
- Skilled communicator and translator, able to articulate the value of complex platform capabilities to non-technical stakeholders and communicate clearly across distributed teams.
- Sharp instinct for internal product management, understanding that other teams' success is your success metric, combined with strong end-user empathy.
- Highly organized, self-directed, and outcomes-focused; comfortable making prioritization trade-offs.
- Illustrated history of living the values necessary to Priceline: Customer, Innovation, Team, Accountability and Trust.
- Unquestionable integrity and ethics.