Principal Product Manager - Conversation AI

Role Overview

For most SMBs, a customer conversation is not merely a support interaction. It is where a lead is qualified, an appointment is booked, a customer question is resolved, a sale is advanced, or a relationship is lost. Businesses receive these conversations across SMS, email, WhatsApp, Facebook, Instagram, live chat, and other messaging channels. Customers expect immediate, accurate, and context-aware responses - but most SMB teams cannot monitor every channel around the clock. HighLevel's Conversation AI product is designed to close that gap.

Conversation AI enables businesses to deploy AI agents that can answer questions, nurture and qualify leads, collect structured information, book and manage appointments, trigger workflows, follow up with inactive contacts, route conversations between specialised agents, and hand interactions to humans when appropriate. The product supports multiple ways of building agents - from guided forms and configurable prompts to visual conversation flows and AI-assisted agent creation. It also includes knowledge retrieval, multi-channel deployment, agent actions, routing, testing, analytics, logs, permissions, reusable snapshots, and APIs for deployment at agency scale.

We are hiring a Principal Product Manager to define the next generation of this platform. This person will own the long-term product strategy for Conversation AI and establish the architecture, product principles, quality systems, operating metrics, and commercial model required to make AI agents dependable across millions of real customer interactions.

This is not a chatbot feature-management role. It is a senior individual-contributor product leadership role at the intersection of AI agents, CRM, messaging infrastructure, workflow automation, knowledge retrieval, appointment scheduling, analytics, developer platforms, and agency-led distribution. You will be expected to influence multiple product and engineering teams, make difficult platform-level trade-offs, and turn Conversation AI into one of the most valuable and trusted products in the HighLevel ecosystem.

Key Responsibilities

• Define and drive the long-term product vision, strategy, roadmap, and product architecture for HighLevel Conversation AI.
• Own the complete Conversation AI system, including agent creation, prompts, knowledge, actions, visual flows, channel deployment, routing, testing, monitoring, permissions, templates, APIs, and lifecycle management.
• Establish the product principles that determine when Conversation AI should answer, ask a clarifying question, take an action, transfer to another agent, follow up later, or hand the conversation to a human.
• Develop a deep understanding of agencies, SMBs, multi-location businesses, and conversation-heavy industries such as home services, legal, dental, medical and wellness, real estate, insurance, automotive, fitness, and professional services.
• Spend significant time reviewing real customer conversations, AI failures, support tickets, agent configurations, implementation challenges, and measurable customer outcomes.
• Translate ambiguous customer and platform problems into clear strategic choices, product requirements, decision documents, system models, phased roadmaps, and measurable success criteria.
• Partner closely with Engineering and AI leadership on LLM orchestration, model selection, context management, tool invocation, retrieval, re-ranking, latency, caching, reliability, evaluation, observability, data architecture, and cost.
• Build a scalable evaluation framework covering grounded accuracy, action accuracy, instruction adherence, safety, escalation quality, conversation quality, and business outcomes.
• Define testing and release standards for changes to models, prompts, retrieval systems, agent tools, routing logic, and conversation behaviour.
• Partner with Design to simplify the entire agent lifecycle - discovery, creation, configuration, training, testing, deployment, debugging, optimisation, and reuse.
• Create a coherent product experience across guided forms, prompt-based agents, flow-based agents, templates, snapshots, and Ask AI-assisted creation.
• Improve integrations between Conversation AI and Contacts, Conversations, Calendars, Workflows, Opportunities, Payments, Knowledge Base, Custom Fields, Custom Objects, and Reporting.
• Define the platform model for multiple agents, channel assignments, routing priorities, bot transfers, context preservation, and human handovers.
• Develop robust observability and debugging experiences that show conversation context, model responses, retrieved knowledge, tool calls, action inputs and outputs, latency, errors, and execution timelines.
• Define instrumentation across the complete product funnel, including agent creation, training, testing, deployment, first successful conversation, first successful action, ongoing usage, customer outcomes, retention, and expansion.
• Own product decisions involving privacy, data retention, access controls, consent, opt-outs, sensitive information, channel policies, model behaviour, and abuse prevention.
• Partner with Product Marketing on positioning, packaging, use cases, competitive differentiation, launch strategy, customer education, and agency enablement.