About the job
Zero Touch Support exists to make working with your IT department easy and pain-free. That is a bigger surface than any one product. It starts at the front door, where Moveworks, Employee Slate, and our chat experiences meet employees inside the tools they already use and resolve most of what they need before a ticket ever exists. When something does need real work, our AI Specialists pick it up, carry it end to end, and close it. Voice AI does the same for the people who would still rather pick up the phone. Employee Insights shows us and our customers where the friction actually sits, so the whole system improves on a loop instead of on a release cycle. Put together, that is the difference between an employee filing a ticket and waiting, and an employee just getting on with their day.
Responsibilities
Own an AI-Native product end to end, from the customer problem through launch criteria to how it performs in production.
Deploy forward by embedding with large IT organizations to build in their environment against real data and analyze AI Agent execution logs.
Define what “good” means for an autonomous agent alongside engineering and AI Quality CoE, including setting defensible customer promises.
Own outcome metrics and ensure instrumentation exists to measure whether shipped features moved them.
Run design partnerships with enterprises and integrate learnings into the roadmap.
Write the strategy, specs, and analysis yourself while using AI tools to prototype, draft, and communicate.
Coach other product managers and raise the bar for how the team works.
Qualifications
Minimum
Have owned an AI-Native product end to end and been the person accountable for its quality bar.
Have shipped something built on LLMs to paying customers and understand enterprise-grade AI requirements including quality bar, guardrails, and telemetry.
Can do your own analysis, including pulling your own data and understanding how a metric was built.
Stay curious and can find the signal in the noise.
Document your position clearly and justify your approach through research and documentation.
Are willing to own the outcome when the org chart is ambiguous about who should.
Preferred
Agent evaluation experience including eval set design, LLM-as-judge, and closing the gap between offline results and production behavior.
Forward-deployed, applied AI, or embedded delivery experience building inside a customer’s environment and generalizing solutions.
IT service management, service desk operations, or enterprise support experience.
Voice agents or conversational products experience where latency and turn-taking matter.
Experience taking a product from zero to one where users, quality bar, and economics had to be worked out simultaneously.
AI-native working habits using AI assistants and productivity tools to prototype, draft, analyze, and automate day-to-day tasks.