Principal Product Manager - Conversation AI

Role Overview\nFor 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.\n\nConversation 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.\n\nWe 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.\n\nThis 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.\n\nKey Responsibilities\n- Define and drive the long-term product vision, strategy, roadmap, and product architecture for HighLevel Conversation AI.\n- 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.\n- 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.\n- Develop a deep understanding of agencies, SMBs, multi-location businesses, and conversation-heavy industries.\n- Spend significant time reviewing real customer conversations, AI failures, support tickets, agent configurations, implementation challenges, and measurable customer outcomes.\n- Translate ambiguous customer and platform problems into clear strategic choices, product requirements, decision documents, system models, phased roadmaps, and measurable success criteria.\n- 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.\n- Build a scalable evaluation framework covering grounded accuracy, action accuracy, instruction adherence, safety, escalation quality, conversation quality, and business outcomes.\n- Define testing and release standards for changes to models, prompts, retrieval systems, agent tools, routing logic, and conversation behaviour.\n- Partner with Design to simplify the entire agent lifecycle — discovery, creation, configuration, training, testing, deployment, debugging, optimisation, and reuse.\n- Create a coherent product experience across guided forms, prompt-based agents, flow-based agents, templates, snapshots, and Ask AI-assisted creation.\n- Improve integrations between Conversation AI and Contacts, Conversations, Calendars, Workflows, Opportunities, Payments, Knowledge Base, Custom Fields, Custom Objects, and Reporting.\n- Define the platform model for multiple agents, channel assignments, routing priorities, bot transfers, context preservation, and human handovers.\n- 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.\n- 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.\n- Own product decisions involving privacy, data retention, access controls, consent, opt-outs, sensitive information, channel policies, model behaviour, and abuse prevention.\n- Partner with Product Marketing on positioning, packaging, use cases, competitive differentiation, launch strategy, customer education, and agency enablement.\n- Partner with Support, Implementation, Trial Experience, Account Management, and Affiliates to reduce setup friction, improve customer outcomes, lower support burden, and make Conversation AI easier to sell and implement.\n- Partner with Finance and Revenue Experience on subscription plans, usage-based pricing, agency rebilling, AI costs, gross-margin targets, and commercially sustainable product limits.\n- Influence product strategy across the broader AI Employee organisation, ensuring that Conversation AI works coherently with Voice AI, Ask AI, Knowledge Base, Agent Studio, Workflow AI, Reviews AI, and future AI products.\n- Act as a senior product thought partner to Product, Engineering, Design, Data, GTM, and executive leadership.\n- Mentor PMs and raise the quality of product thinking, AI evaluation, strategy, and decision-making across the organisation without relying on formal authority.\nIdeal Candidate Profile\n- 12+ years of product management experience owning complex B2B SaaS, AI-agent, communications, CRM, workflow-automation, customer-support, or platform products. Scope and demonstrated outcomes matter more than a precise number of years.\n- Prior experience operating as a Principal PM, Staff PM, Lead PM, product founder, or equivalent senior individual contributor responsible for a business-critical product or platform.\n- Evidence of defining product strategy across multiple teams and delivering sustained business outcomes - not only shipping individual features.\n- Strong understanding of AI-agent product design, including the difference between language generation and dependable execution of business tasks.\n- Experience working with one or more of the following:\n○ Conversational AI\n○ AI agents and tool use\n○ Customer-service automation\n○ CRM and customer communications\n○ Workflow or journey automation\n○ Knowledge retrieval and RAG\n○ Developer platforms\n○ Messaging or contact-centre products\n- Technical fluency sufficient to make credible decisions with engineering teams about LLM behaviour, retrieval, orchestration, APIs, data models, latency, evaluation, observability, reliability, and model economics.\n- Strong judgment around AI quality and trust — able to define how an AI system should be tested, monitored, debugged, and improved in production.\n- Strong systems thinking — able to connect conversations, contacts, calendars, workflows, opportunities, knowledge, permissions, billing, analytics, and channel infrastructure into a coherent product.\n- Ability to distinguish between a compelling AI demonstration and a product that can operate safely and reliably across millions of customer interactions.\n- Experience simplifying technically sophisticated systems for non-technical customers.\n- Strong understanding of multi-tenant SaaS and the operational requirements of agencies, franchises, multi-location businesses, or reseller ecosystems.\n- Strong customer instincts and willingness to personally inspect conversations, logs, support issues, implementation problems, and product data.\n- Commercial judgment across adoption, packaging, pricing, retention, AI costs, gross margin, and partner economics.\n- Ability to influence senior stakeholders and multiple teams without relying on direct reporting authority.\n- Excellent written communication — able to produce clear strategy documents, product requirements, architectural trade-off decisions, evaluation plans, executive updates, and launch narratives.\n- High ownership, intellectual honesty, and execution velocity in a remote-first, high-context environment.\n- Comfortabile making difficult prioritisation decisions across customer experience, AI quality, revenue, platform extensibility, technical debt, compliance, and cost.\nBonus Points For\n- Experience building production AI agents that use tools, APIs, workflows, or structured actions — not only conversational interfaces.\n- Experience with LLM orchestration, prompt and policy layers, function calling, context management, memory, model routing, agent planning, or multi-agent systems.\n- Experience building RAG or knowledge platforms involving embeddings, vector retrieval, re-ranking, source attribution, freshness, and evaluation.\n- Experience building AI evaluation systems using test datasets, simulations, human review, production telemetry, and business-outcome measurement.\n- Experience with messaging channels and infrastructure such as SMS, email, WhatsApp, Facebook Messenger, Instagram messaging, web chat, or contact-centre software.\n- Experience with appointment scheduling, service marketplaces, lead qualification, customer support, or sales-assistant products.\n- Experience building workflow automation products similar to HighLevel Workflows, Zapier, Make, HubSpot Workflows, Salesforce Flow, or customer-journey builders.\n- Experience with APIs, webhooks, developer tools, reusable templates, marketplaces, or partner ecosystems.\n- Experience building products for agencies, SMBs, franchises, local businesses, or multi-location operators.\n- Experience with AI usage-based pricing, model cost management, gross-margin optimisation, subscription packaging, rebilling, or fair-use controls.\n- Familiarity with metrics such as grounded-answer accuracy, containment rate, escalation rate, action success rate, conversation completion, booking conversion, lead conversion, response latency, P95/P99 reliability, cost per successful outcome, retention, and support-ticket rate.\nWhy Join HighLevel?\n- At HighLevel, we foster an exciting and dynamic work environment driven by a passionate team. We believe in a collective responsibility where no task is considered someone else's job. Our unwavering focus is on providing value to our users, and we achieve this by delivering solutions swiftly through lean principles, allowing us to bring products to market in weeks rather than quarters.\n- Every good idea is put to the test, ensuring that we maintain a high standard of innovation. We prioritise the well-being of our team, recognising that by taking care of them, they can better serve our users. We embrace the concept of continuous and iterative improvement, understanding that progress is an ongoing journey. We are also a well-funded and profitable company.\n- Join us at HighLevel, and you will have the opportunity to learn the intricacies of scaling a B2B SaaS startup and develop impactful products that cater to the needs of our customers.\nEEO Statement\nThe company is an Equal Opportunity Employer. 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