Senior Principal Product Manager

Zendesk Zendesk · Enterprise · Melbourne, Australia

Senior Principal Product Manager to lead the product strategy and roadmap for Zendesk's internal AI Platform. This platform supports the full agent lifecycle, providing shared capabilities, infrastructure, SDKs, and developer tooling for vertical AI teams. The role involves driving platform adoption, managing experimentation and evaluation, and ensuring AI security and compliance.

What you'd actually do

  1. Own the end-to-end product strategy and roadmap for Zendesk's internal AI Platform, a layered stack spanning foundational infrastructure through to developer-facing tooling:
  2. Drive platform adoption across vertical AI teams — meeting teams where they are and enabling progressive adoption of shared capabilities to help them meet their goals
  3. Lead the development of Zendesk's A2A (Agent-to-Agent) layer and agent registry, enabling connection, reuse, and governance across existing agents and applications.
  4. Conduct internal user research and external industry research to define a forward-thinking roadmap that sets Zendesk up to accelerate AI development in the short and long-term.
  5. Collaborate with stakeholders to translate evolving machine learning and AI platform needs into clear, actionable requirements that balance innovation, reliability, and cost efficiency.

Skills

Required

  • 5+ years of product development experience, with significant experience in developer platforms, AI/ML infrastructure, or internal tooling at scale.
  • Deep understanding of AI/ML concepts, model lifecycle, agent architectures, distributed execution, and platform engineering.
  • Technical background and 10 yrs product management experience.
  • Proven ability to drive platform adoption and developer experience for internal customers — you understand what makes engineers want to use a platform vs. build their own.
  • Experience leading multi-layered platform initiatives with strong strategic vision, from low-level infrastructure through to developer-facing SDK/API design.
  • Demonstrated success managing experimentation and evaluation platforms, developer-facing APIs, agent frameworks, or AI developer ecosystems.
  • Exceptional communication and stakeholder management skills enabling influence across engineering, data science, security, and product teams.
  • Skilled in balancing competing priorities, managing ambiguity, and driving consensus in complex environments.

Nice to have

  • Experience with building agentic features and experiences, AI/ML workloads
  • Background in building developer tooling — SDKs, CLIs, registries, or platform-as-a-product for internal engineering teams.
  • Familiarity with multi-vendor large language model (LLM) serving platforms and failover strategies.
  • Experience with AI security, privacy, and compliance (data guardrails, filtering, trust metrics).

What the JD emphasized

  • internal AI Platform
  • developer platforms
  • AI/ML infrastructure
  • internal tooling at scale
  • agent architectures
  • platform engineering
  • developer experience
  • internal customers
  • multi-layered platform initiatives
  • experimentation and evaluation platforms
  • agent frameworks
  • AI developer ecosystems
  • AI security, privacy, and compliance
  • data guardrails
  • filtering
  • trust metrics

Other signals

  • AI Platform
  • Agent Development Kit (ADK) & SDK
  • Platform Capabilities
  • Evaluation & Observability
  • Infrastructure
  • agent registry
  • internal user research
  • developer platforms
  • AI/ML infrastructure
  • internal tooling at scale
  • agent architectures
  • distributed execution
  • platform engineering
  • developer experience
  • internal customers
  • multi-layered platform initiatives
  • experimentation and evaluation platforms
  • developer-facing APIs
  • agent frameworks
  • AI developer ecosystems
  • AI security, privacy, and compliance
  • data guardrails
  • filtering
  • trust metrics