Senior Product Manager (AI)

Weekday Inc; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Weekday Inc. in San Francisco, CA, is seeking a Senior Product Manager (AI) to own strategy, roadmap, and execution of AI product capabilities.

You will mold how agents detect, triage, and resolve data incidents and collaborate across teams and enterprise tools. The role requires hands-on leadership, experience with LLM-powered products, and a track record of delivering reliable agentic systems in production.

Qualifications

• Hands-on experience building and deploying AI-powered applications or agent-based workflows.
• 3+ years of Product Management experience on complex AI/ML platforms, data tools, APIs, or developer tools.
• Strong understanding of foundation models, cost, latency, and quality trade-offs.
• Experience with prompt engineering, context engineering, and evaluating model performance.

Responsibilities

• Own AI product strategy and roadmap, defining autonomous workflows for agents.
• Evaluate feasibility, scalability, and maintainability of agent features.
• Define autonomy boundaries and human oversight requirements for actions.
• Translate customer needs and constraints into detailed PRDs for engineering.
• Collaborate with engineering to design, evaluate, and launch AI agents meeting high standards.

Skills

LLM-powered apps

Agentic workflows

Product management

Foundation models

Prompt engineering

Education

Bachelor or higher in CS/DS/Engineering

Tools

Teams

Slack

ServiceNow

Jira

Job description

San Francisco, United States | Posted on 11/02/2026

We are looking for a technically strong and hands-on Senior Product Manager (AI) to own the strategy, roadmap, and execution of our AI product capabilities. This role sits at the intersection of product leadership and applied AI engineering, with a focus on building reliable, autonomous agentic systems for enterprise data operations.

You will define how AI agents detect, investigate, triage, and resolve data incidents. This includes determining the context available to agents, the actions they can safely perform, how their outputs are evaluated, and where human oversight is required. You will work across context engineering, resolution and triage agents, and MCP-based integrations with enterprise tools such as Microsoft Teams, Slack, ServiceNow, and Jira.

The ideal candidate has direct experience building and shipping LLM-powered products and understands what makes agentic systems reliable in production. You should be comfortable evaluating model capabilities, designing tool and API interfaces, engineering context, building evaluation frameworks, and balancing cost, latency, quality, and user experience.

This is a hands-on product leadership role that requires the ability to understand technical failures, identify their root causes, set product direction, and translate complex AI capabilities into precise requirements that engineering teams can execute.

Requirements

Key Responsibilities

• Own the AI product strategy and roadmap, identifying workflows that agents can independently manage, assist with, or should not handle.
  
• Evaluate the technical feasibility, potential failure modes, scalability, and long-term maintainability of agentic product features.
  
• Define agent autonomy boundaries, including which actions can be performed independently and which require human approval or intervention.
  
• Translate customer requirements, technical constraints, and evolving model capabilities into detailed product requirement documents (PRDs).
  
• Partner closely with engineering teams to design, develop, evaluate, and launch AI agents that meet high standards for accuracy, latency, cost efficiency, observability, and security.
  
• Build and manage the AI evaluation framework, including golden datasets, quality rubrics, and regression testing to ensure model and prompt changes are supported by measurable evidence.
  
• Recreate real-world customer data environments and incident scenarios to validate agent performance and production readiness.
  
• Define and monitor AI product success metrics, including resolution accuracy, acceptance rates, time to root cause, incident deflection, and token cost per outcome.
  
• Analyse product performance, diagnose issues, and continuously improve agent behaviour based on data, customer feedback, and evaluation results.
  
• Collaborate with Go-to-Market teams on product launches, technical enablement, and customer-facing documentation.
  
• Establish transparency around agent decisions by ensuring users can understand the context, reasoning, and evidence behind recommendations and actions.
  
• Stay updated on foundation models, agent frameworks, MCP, tool-calling standards, retrieval techniques, and AI evaluation methodologies.
  
• Champion a trustworthy and intuitive AI experience for data engineers and analysts, ensuring AI capabilities remain transparent, explainable, and correctable at scale.
  

Required Qualifications

• Hands-on experience building and deploying LLM-powered applications or agentic workflows in a technical capacity.
  
• At least 3 years of Product Management experience working on technically complex products such as AI/ML platforms, data platforms, developer tools, or APIs.
  
• Strong working knowledge of foundation models, their capabilities, limitations, and cost, latency, and quality trade-offs.
  
• Understanding of agent architecture, including tool calling, context assembly, retrieval, orchestration, and guardrails.
  
• Experience with prompt engineering, context engineering, and evaluating model performance.
  
• Strong understanding of evaluation methodologies for non-deterministic AI systems.
  
• Ability to identify where AI can deliver meaningful value and where conventional software or human intervention is more appropriate.
  
• Proven ability to work closely with engineering teams and make informed technical and product trade-offs.
  
• Excellent communication skills, with the ability to explain complex technical concepts to both technical and non-technical stakeholders.
  
• Bachelor's degree in Computer Science, Data Science, Information Systems, Engineering, or a related technical discipline, or equivalent practical experience.
  

Preferred Qualifications

• Experience working with MCP, agent frameworks, or integrating AI capabilities into third-party platforms such as Teams, Slack, ServiceNow, and Jira.
  
• Previous ownership of an AI or agent-based product within a B2B SaaS or data-focused organisation.
  
• Familiarity with data observability, data lineage, governance, metadata management, or related platforms.
  
• Hands-on experience in data engineering or data architecture, or experience developing AI solutions on enterprise data systems.
  
• Experience working in a high-growth SaaS, technology, or developer-focused product environment.