AI Product Manager

MSPbots•Chicago, IL

27d•Remote

About The Position

We aren't just adding "wrappers" to our software; we are rebuilding the MSP (Managed Service Provider) experience from the ground up. You will be the CEO of our AI Product Suite, responsible for turning LLMs and agentic workflows into high-margin, revenue-generating engines. This role is for a builder who treats "time-to-value" as a North Star and "ARR" as the ultimate validation of product-market fit.

Requirements

3+ Years of AI/SaaS PM Experience: You have shipped at least one product powered by LLMs, RAG (Retrieval-Augmented Generation), or Agentic frameworks.
Commercial Instinct: You have a track record of owning a P&L or being directly responsible for revenue KPIs. You understand CAC, LTV, and churn.
Technical Depth: You don't need to write the Python, but you should understand tokens, embeddings, prompt engineering, and the difference between "hallucination" and "creative temperature."
Global Collaborator: Proven ability to lead sync and async workflows across US, China, and Philippines time zones.

Nice To Haves

MSP/ITSM Domain Knowledge (Bonus): Familiarity with how tickets flow through a service desk is a massive plus.

Responsibilities

Revenue & Market Strategy (The "Business" Owner)
Monetization Architecture: Move beyond flat SaaS seats. Design and implement usage-based pricing, credit systems, or "value-based" billing tied to AI-automated outcomes.
Competitive Intelligence: Maintain a "Battlecard" mentality. Know exactly what the incumbents and startups are shipping, and ensure our AI capabilities (triage, QA, and automation) are 10x better, not 10% faster.
GTM Execution: Don't just hand off a deck to Sales. Drive the narrative, define the ICP (Ideal Customer Profile), and ensure our positioning clearly articulates the ROI of "AI over Headcount."
AI Native Product Leadership
The "AI Trinity" Tradeoff: Ruthlessly manage the balance between Accuracy, Latency, and Unit Cost. You know when to use a frontier model (GPT-4/Claude 3.5) vs. a fine-tuned small language model (SLM) for specific MSP tasks.
Agentic Workflows: Shift from simple "chatbots" to autonomous agents that can execute ticket resolution, not just summarize it.
Data as a Moat: Define the data collection strategy to create a proprietary feedback loop (RLHF) that makes our models smarter with every MSP ticket handled.
Rapid Execution & Experimentation
0 → 1 Execution: Lead "Flash Builds"—getting prototypes in front of customers in days, not months.
Performance Benchmarking: Define what "Good" looks like. Build the evaluation frameworks (Evals) to ensure our AI isn't just "cool" but consistently reliable in a production MSP environment.