Director, AI Product Management

Position Responsibilities

Business ownership: Ensure the agents are monetizable and a commercial success. You will drive the ideation, design, and development of AI-powered agents and solutions, with a focus on creating monetizable agentic capabilities aligned to MSP market needs.
Roadmap ownership: Own the Rai product roadmap from discovery through delivery, balancing near-term partner value with the longer-term platform convergence vision.
Agentic workflow definition: Define and prioritize agentic workflows that move MSPs from visibility to decision to action, replacing or extending MSP workforce jobs rather than just adding a chat interface.
AI evaluation and quality: Establish evaluation frameworks for AI features, including how WatchGuard defines quality bars, measures accuracy and reliability, and decides when an automated action is ready for production.
Customer discovery: Work directly with MSP partners to understand workflows and pain points, and validate product direction through direct customer engagement.
Cross-functional alignment: Drive alignment across engineering, channel, PMM, and leadership using working prototypes and real partner feedback. Clearly conveying the outcomes to each stakeholder.
AI safety and reliability: Own AI safety and reliability as a product responsibility, including how Rai behaves when confidence is low, when actions are irreversible, and when the MSP’s trust is on the line.
Go-to-market partnership: Partner with PMM, channel, and sales to translate product capability into GTM strategy, partner messaging, and enablement.
Performance monitoring: Define and track success metrics for Rai features: automation rate, ticket deflection, time-to-action, accuracy, and downstream business outcomes for MSPs.
Competitive intelligence: Monitor the AI and MSP platform competitive landscape to identify differentiation opportunities.

Required Qualifications

MSP market knowledge: Deep familiarity with how managed service providers operate, including how they structure their teams, price and deliver services, manage margin pressures, and where technician time goes. You understand that for MSPs, simplicity and automation are not features; they are the business case. You know the difference between a tool an MSP will actually adopt and one that adds process to an already stretched team.
Agentic AI product experience: Demonstrated depth in product management, with meaningful hands-on experience shipping agentic AI or LLM-powered automation in a B2B context — typically 8+ years overall and at least 2 years working directly on autonomous or semi-autonomous AI workflows where the system takes action on behalf of the user. Candidates who have shipped real agentic products recently will be weighted over those with tenure alone.
LLM technical fluency: Hands-on familiarity with how LLMs work in production: context limits, latency tradeoffs, hallucination risks, and when RAG, fine-tuning, or deterministic fallbacks are the right answer.
AI evaluation and governance: Experience defining evaluation criteria and quality standards for AI actions, including how to validate that an automated workflow is safe to run unsupervised.
AI-native product approach: AI tools are part of your core workflow. You use them for customer research synthesis, Spec Driven Design, and prototype validation, getting to a well-structured spec faster and with more rigor than traditional methods allow. You think in terms of what AI can own end-to-end, not just where it can assist.
Product instincts: Strong instincts for what makes an agentic feature genuinely useful versus impressive in a demo, especially in an MSP context where trust, reliability, and low-friction adoption determine whether a product survives the first 90 days.
Cross-functional collaboration: Comfort working across engineering, design, and go-to-market in a fast-moving environment.
Communication skills: Clear, direct communicator who can move between technical depth and business narrative depending on the audience.

Nice to Have

PSA and RMM familiarity: Experience with MSP operational tooling including ConnectWise, Autotask, NinjaOne, or HaloPSA.
Security or platform background: Background in cybersecurity products, managed services, or multi-tenant SaaS platforms.
Prototyping experience: Experience building or evaluating functional prototypes as a discovery and alignment tool.
Responsible AI: Understanding of responsible AI in production environments, including explainability, auditability, rollback behavior, and least-privilege action design.