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

Smartsheet has been empowering teams to manage work seamlessly for over 20 years, and they are seeking a Senior Product Manager II to join their Applied AI organization. The role involves owning the product roadmap for the AI platform, bridging teams, and ensuring the governance and quality of AI features.

Responsibilities:

• Own the AI platform roadmap: 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
• Bridge platform and product teams: Own the seams between infrastructure and the teams shipping AI-powered product features, ensuring agent capabilities are governed, discoverable, and consistently delivered
• Own AI monetization as a product surface: Map agent activity to consumption-based AI credits, ensure logging infrastructure supports that mapping, and surface usage and cost data to enterprise admins
• Make evaluation and observability first-class: Ship pre-deployment eval gates plus production tracing, monitoring, alerting, and regression detection so you catch quality issues before customers do
• Build the AI governance and responsible AI layer: 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
• Define the AI admin experience: Own the controls governing which AI features are on or off and for whom, and which usage, cost, and audit data gets surfaced to enterprise admins

Requirements:

• 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
• 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