Sr. Product Manager II - AI Platform (remote Eligible)

Smartsheet Smartsheet · Seattle · United States · Product Mgt

Product Manager for an AI platform focused on agent orchestration, model serving, evaluation, governance, and monetization within an enterprise SaaS context. The role bridges platform infrastructure with product teams and defines the AI admin experience.

What you'd actually do

  1. Define and drive strategy and execution for model serving, the LLM gateway, agent orchestration, and the infrastructure that keeps models and agents running reliably in production -- partnering with data engineering to define what the AI platform needs from the data layer without owning it directly.
  2. Own the seams between infrastructure and the teams shipping AI-powered product features, ensuring agent capabilities are governed, discoverable, and consistently delivered.
  3. Map agent activity to consumption-based AI credits, ensure logging infrastructure supports that mapping, and surface usage and cost data to enterprise admins.
  4. Ship pre-deployment eval gates plus production tracing, monitoring, alerting, and regression detection so you catch quality issues before customers do.
  5. Own output-quality guardrails, agent access controls, data classification, audit logging, and enterprise compliance (SOC 2, EU AI Act, ISO 42001) as a horizontal control plane across every AI feature -- including the responsible AI framework governing how we evaluate bias, fairness, and safe agent behavior.

Skills

Required

  • 8+ years of product management experience with technically complex platforms, ideally in enterprise SaaS
  • Deep fluency in AI/ML concepts -- agent and sub-agent architectures, orchestration, tool use, prompt engineering, and quality measurement
  • Track record of shipping enterprise software from inception to launch with measurable business impact
  • Comfort operating in high-ambiguity environments with significant autonomy and the ability to influence without authority across engineering, security, legal, and executive stakeholders

Nice to have

  • Experience building or managing evaluation and quality infrastructure for AI or ML systems, including reading traces and eval runs to form your own opinion on technical tradeoffs
  • Familiarity with data and ML platforms, particularly Databricks (Delta Lake, Unity Catalog, MLflow) or equivalent lakehouse environments
  • Demonstrated experience with responsible AI frameworks -- governance policy, agent access controls, audit logging, or enterprise AI compliance

What the JD emphasized

  • AI agents
  • agent orchestration
  • evaluation
  • governance
  • responsible AI
  • enterprise compliance
  • SOC 2
  • EU AI Act
  • ISO 42001

Other signals

  • AI agents
  • AI platform
  • model serving
  • agent orchestration
  • evaluation
  • governance
  • monetization