{"id":3290530,"url":"https://alion.io/job/highlevel-principal-product-manager-conversation-ai","title":"Principal Product Manager - Conversation AI","company":{"id":3882884,"name":"HighLevel","domain":"highlevel.co","url":"https://alion.io/company/highlevel-3","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Product","role_family":"Product","seniority":"lead","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["IN"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":124000,"max_usd":261000,"period":"year","method":"global_role_seniority_cell","sample_n":1783},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Function Calling","optional":false},{"name":"LLM","optional":false},{"name":"RAG","optional":false},{"name":"WhatsApp","optional":false},{"name":"Apex","optional":true},{"name":"Embeddings","optional":true},{"name":"Multi-Agent Systems","optional":true},{"name":"Salesforce Flow","optional":true},{"name":"Zapier","optional":true}],"status":"live","first_seen_at":"2026-10-15T17:04:25Z","employer_posted_date":null,"last_verified_at":"2026-10-15T17:04:25Z","board_verified":false,"closed_at":null,"days_open":13,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":13},"description":"About usHighLevel is an AI-powered business operating system that gives agencies, entrepreneurs and SMBs the infrastructure to build, automate and scale. Today, HighLevel supports SMBs across 150+ countries, fueling community-driven growth rooted in real customer outcomes.\nTo date, businesses operating on HighLevel have generated over $7 billion in ecosystem value, demonstrating the impact of shared infrastructure at scale. By centralizing conversations, automation and intelligence into one system, we help businesses move faster, reduce complexity and execute efficiently. Behind the platform, HighLevel powers more than 4 billion API hits and 2.5 billion message events daily. With 250 terabytes of distributed data, 250+ microservices and over 1 million domain names supported, our architecture is built for performance, resilience and long-term scalability.\nOur peopleWith over 2,000 team members across 10+ countries, HighLevel operates as a global, remote-first organization built for speed and ownership. We value initiative, clarity and execution, creating space for ambitious people to build systems that support millions of businesses worldwide. Here, innovation thrives, ideas are celebrated and people come first, no matter where they call home.\nOur impactEvery month, HighLevel enables more than 1.5 billion messages, 200 million leads and 20 million conversations for the more than 1 million businesses we support. Behind those numbers are real people building independence, expanding opportunity and creating measurable impact.\nWe’re proud to be a part of that.Learn more about us on ourYouTube Channel orBlog PostsRole 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.\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.\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.\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\nDefine and drive the long-term product vision, strategy, roadmap, and product architecture for HighLevel Conversation AI.\n\nOwn 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\nEstablish 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\nDevelop 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.\n\nSpend significant time reviewing real customer conversations, AI failures, support tickets, agent configurations, implementation challenges, and measurable customer outcomes.\n\nTranslate ambiguous customer and platform problems into clear strategic choices, product requirements, decision documents, system models, phased roadmaps, and measurable success criteria.\n\nPartner 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\nBuild a scalable evaluation framework covering grounded accuracy, action accuracy, instruction adherence, safety, escalation quality, conversation quality, and business outcomes.\n\nDefine testing and release standards for changes to models, prompts, retrieval systems, agent tools, routing logic, and conversation behaviour.\n\nPartner with Design to simplify the entire agent lifecycle — discovery, creation, configuration, training, testing, deployment, debugging, optimisation, and reuse.\n\nCreate a coherent product experience across guided forms, prompt-based agents, flow-based agents, templates, snapshots, and Ask AI-assisted creation.\n\nImprove integrations between Conversation AI and Contacts, Conversations, Calendars, Workflows, Opportunities, Payments, Knowledge Base, Custom Fields, Custom Objects, and Reporting.\n\nDefine the platform model for multiple agents, channel assignments, routing priorities, bot transfers, context preservation, and human handovers.\n\nDevelop 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\nDefine 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\nOwn product decisions involving privacy, data retention, access controls, consent, opt-outs, sensitive information, channel policies, model behaviour, and abuse prevention.\n\nPartner with Product Marketing on positioning, packaging, use cases, competitive differentiation, launch strategy, customer education, and agency enablement.\n\nPartner 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\nPartner with Finance and Revenue Experience on subscription plans, usage-based pricing, agency rebilling, AI costs, gross-margin targets, and commercially sustainable product limits.\n\nInfluence 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\nAct as a senior product thought partner to Product, Engineering, Design, Data, GTM, and executive leadership.\n\nMentor PMs and raise the quality of product thinking, AI evaluation, strategy, and decision-making across the organisation without relying on formal authority.\n\nIdeal Candidate Profile\n12+ 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\nPrior 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\nEvidence of defining product strategy across multiple teams and delivering sustained business outcomes - not only shipping individual features.\n\nStrong understanding of AI-agent product design, including the difference between language generation and dependable execution of business tasks.\n\nExperience working with one or more of the following:\n\n○ Conversational AI○ AI agents and tool use○ Customer-service automation○ CRM and customer communications○ Workflow or journey automation○ Knowledge retrieval and RAG○ Developer platforms○ Messaging or contact-centre products\nTechnical 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\nStrong judgment around AI quality and trust — able to define how an AI system should be tested, monitored, debugged, and improved in production.\n\nStrong systems thinking — able to connect conversations, contacts, calendars, workflows, opportunities, knowledge, permissions, billing, analytics, and channel infrastructure into a coherent product.\n\nAbility to distinguish between a compelling AI demonstration and a product that can operate safely and reliably across millions of customer interactions.\n\nExperience simplifying technically sophisticated systems for non-technical customers.\n\nStrong understanding of multi-tenant SaaS and the operational requirements of agencies, franchises, multi-location businesses, or reseller ecosystems.\n\nStrong customer instincts and willingness to personally inspect conversations, logs, support issues, implementation problems, and product data.\n\nCommercial judgment across adoption, packaging, pricing, retention, AI costs, gross margin, and partner economics.\n\nAbility to influence senior stakeholders and multiple teams without relying on direct reporting authority.\n\nExcellent written communication — able to produce clear strategy documents, product requirements, architectural trade-off decisions, evaluation plans, executive updates, and launch narratives.\n\nHigh ownership, intellectual honesty, and execution velocity in a remote-first, high-context environment.\n\nComfortable making difficult prioritisation decisions across customer experience, AI quality, revenue, platform extensibility, technical debt, compliance, and cost.\n\nBonus Points For\nExperience building production AI agents that use tools, APIs, workflows, or structured actions — not only conversational interfaces.\n\nExperience with LLM orchestration, prompt and policy layers, function calling, context management, memory, model routing, agent planning, or multi-agent systems.\n\nExperience building RAG or knowledge platforms involving embeddings, vector retrieval, re-ranking, source attribution, freshness, and evaluation.\n\nExperience building AI evaluation systems using test datasets, simulations, human review, production telemetry, and business-outcome measurement.\n\nExperience with messaging channels and infrastructure such as SMS, email, WhatsApp, Facebook Messenger, Instagram messaging, web chat, or contact-centre software.\n\nExperience with appointment scheduling, service marketplaces, lead qualification, customer support, or sales-assistant products.\n\nExperience building workflow automation products similar to HighLevel Workflows, Zapier, Make, HubSpot Workflows, Salesforce Flow, or customer-journey builders.\n\nExperience with APIs, webhooks, developer tools, reusable templates, marketplaces, or partner ecosystems.\n\nExperience building products for agencies, SMBs, franchises, local businesses, or multi-location operators.\n\nExperience with AI usage-based 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