VP of Product for AI Platform

Description

The company is entering a new phase as an established enterprise software business, rethinking how it builds, ships and runs technology in an AI-native market.

AI and agent technologies are changing enterprise software in ways that go beyond adding AI features to current products. The underlying platforms, architecture, data foundations and technical capabilities need rethinking so systems can understand context, reason, act and produce business results.

The company is investing in this shift to build a smarter, more connected and adaptable platform that speeds up product innovation, creates new customer value and makes operations faster and leaner.

This is a VP of Product for AI Platform role. You will help define and build that foundation, combining deep technical knowledge, platform strategy and an entrepreneurial approach.

You will set the strategy, architecture and development of the shared AI platform capabilities behind the company's next generation of products.

This goes beyond integrating large language models or deploying AI infrastructure. You will decide what to build, what to take from the fast-moving AI ecosystem, and how to create a scalable platform so product teams can move quickly without fragmenting the architecture or duplicating core work.

You will work with Product, Engineering, Data, Security and other technology leaders to support AI-powered workflows, intelligent automation and agent experiences. You will turn an ambitious AI-native vision into a practical technical plan covering architecture, investment, build or buy choices and the capabilities that set the company apart. The role is strategic and hands-on: set direction, build core capabilities and move the organization from experimentation to production at scale.

Responsibilities

• Set the vision, strategy and technical architecture for the AI platform, aligned with the company's wider AI-native transformation.
• Identify the core capabilities needed for AI-powered products, smart workflows and agent applications across the portfolio.
• Design a platform that supports reuse, interoperability, extensibility and fast delivery across product teams.
• Decide when to use outside models, services and infrastructure, and when to invest in in-house capabilities that differentiate the company.
• Plan a roadmap that weighs near-term product opportunities against the long-term platform needs of the company.
• Create shared capabilities so product teams can add AI to customer experiences and workflows without rebuilding base infrastructure for each use case.
• Lay the technical groundwork for agents that read user intent, pull relevant context, reason through tasks, use enterprise systems and take action.
• Define how models are integrated and orchestrated, how context is managed, and how retrieval-augmented generation, tool use and agent execution work.
• Give teams secure, reliable access to enterprise data, business logic, APIs and transactional functions.
• Build reusable patterns and services that speed up experiments and shorten the path from prototype to production.
• Support both conventional AI-enabled experiences and more autonomous, multi-step agent workflows as the technology matures.
• Build a secure, reliable and scalable platform that meets enterprise customer requirements and business-critical workflows.
• Set standards for model evaluation, testing, observability, performance and continuous improvement.
• Manage AI quality, latency, availability and cost, including model selection and routing.
• Work with Security, Legal, Privacy and Compliance to set controls over data access, model use, agent permissions and execution.
• Keep AI systems within defined authorization limits, with safeguards, traceability and human oversight for consequential actions.
• Monitor production performance, catch failures, assess outcomes and improve system behavior over time.
• Turn platform investment into measurable business and customer results, such as faster product development, better customer experiences, more automation and leaner operations.
• Track platform adoption, developer productivity, reliability, AI quality, cost efficiency and time to production.
• Rank platform investments by business impact and by the number of product teams they unlock.
• Keep the platform evolving as AI technology advances, customer needs shift and lessons come in from production.
• Explain architectural decisions, investment priorities, trade-offs and progress clearly to executive leadership.
• Partner with Product and Engineering leaders to find where shared AI capabilities can unlock customer value and speed up innovation, and help teams rethink what becomes possible when AI interprets intent and carries out workflows.
• Provide clear platform interfaces, development patterns, documentation and tools so product engineers can use AI capabilities easily.
• Reduce duplicated infrastructure, fragmented implementations and unnecessary complexity across teams.
• Help define how platform engineering, AI research and application development, product management and data teams work together.
• Encourage rapid experimentation, disciplined engineering and learning from production outcomes.
• Build and lead a high-impact AI platform organization with technical depth, strong engineering judgment and an outcome focus.
• Recruit and retain talent across AI infrastructure, platform engineering, distributed systems and applied AI as the mandate evolves.
• Foster ownership, technical excellence, accountability and pragmatic execution, so teams move quickly while meeting enterprise standards for reliability, security and discipline.
• Build strong partnerships across the company, gaining influence through technical credibility and a clear case for platform value.

Requirements

• You are a technology leader who can build the AI platform that makes the company's transformation real, turning fast-changing AI capabilities into secure, reusable, enterprise-grade foundations that let products understand intent, run workflows and deliver outcomes.
• You have a proven record building and scaling platforms, infrastructure or distributed systems in complex enterprise software environments.
• You have a strong understanding of modern AI architecture and the infrastructure needed for production-grade AI applications.
• You are familiar with large language models, model serving and orchestration, retrieval-augmented generation, embeddings, vector search, context engineering and agentic application architectures.
• You have designed systems that connect AI with enterprise data, APIs, business logic and transactional systems.
• You have a solid grounding in cloud architecture, distributed systems, security, reliability and scalable software engineering.
• You can separate lasting architectural principles from capabilities that are changing rapidly as AI evolves.
• You understand that AI-native software requires more than adding a model or chat interface to an existing application.
• You think in reusable capabilities, intelligent workflows, agents, orchestration, and systems that act for users within defined boundaries.
• You can tell which capabilities belong in shared platform services and which should stay specific to a product or use case.
• You understand the role of context, permissions, data access, evaluation, observability and reliability when AI moves from demos into enterprise workflows. You take a practical view of what current AI systems can do and design for rapid change.
• You have taken complex technical initiatives from concept through architecture, development, production deployment and wide adoption.
• You combine strategic thinking with hands-on engagement in technical decisions, and you challenge assumptions.
• You make clear trade-offs between speed, flexibility, cost, reliability and long-term architectural integrity.
• You know when to build, when to buy, when to partner and when to wait for technology to mature.
• You are comfortable when the destination is clear but the technical and organizational path must still be worked out.
• You see the platform as a means to better products and business outcomes, not technology for its own sake.
• You connect architecture and investment decisions to customer value, product differentiation, developer productivity and commercial impact.
• You work effectively with senior Product and Engineering leaders to identify the highest-value AI opportunities.
• You favor simplicity, reuse and adoption over unnecessarily sophisticated infrastructure.
• You use measurable outcomes to judge whether platform investments are paying off.
• You have built technology that must meet demanding requirements for security, privacy, reliability and enterprise-scale operations.
• You set standards and governance without adding bureaucracy or slowing innovation.
• You explain complex technical concepts clearly to engineers, product leaders and executives.
• You build alignment across organizational boundaries around architecture, priorities and shared ownership.
• You attract strong engineers and create an environment where high performers can do their best work.
• You are curious, adaptable and energized by the pace of change in AI. You do not assume established software architecture or organizational models will stay sufficient.
• You separate real technical opportunities from hype and make sound decisions under uncertainty.
• You set a clear direction without pretending every answer is already known.
• You are motivated by helping define the next generation of enterprise software platforms rather than running an established one.

Conditions

The annualized base salary range for this role is $271,200 - $406,800 USD.

The final base salary falls within that range and depends on qualifications, job-related knowledge, prior related experience, unique skills, the role's location, internal equity and internal budget.

Base pay is one part of total rewards, which may also include bonus or variable compensation, equity and benefits.

Candidates from all backgrounds are encouraged to apply.

Benefits

• A total rewards package designed to support wellbeing, growth and flexibility. Specific offerings vary by country.
• Competitive pay, variable bonus and performance-based rewards, and retirement programs.
• Medical, dental and vision insurance.
• Generous flexible time off, paid holidays, wellness days and a company-wide year-end break.
• Paid parental leave, including fully paid leave for eligible employees, subject to local policy.
• A learning and development stipend.
• Volunteering opportunities and charitable donation matching where available.
• Mental wellbeing resources and support.
• Benefits may vary by location, and details are shared during the interview process.