Principal Product Manager - AI

HighLevel; Dallas (TX); Hybrid; USD 180.000 - 240.000; Vollzeit

Zusammenfassung

HighLevel is seeking a Principal Product Manager to own the long-term product strategy for its intelligence and assistance platform. This leader will define architecture, principles, quality systems, developer model, governance, metrics, and commercial strategy to make Ask AI the default for HighLevel users.

You will shape the intersection of AI assistants, retrieval systems, agent orchestration, CRM data, permissions, automation, developer platforms, and multi-tenant SaaS, influencing multiple

Qualifikationen

• 12+ years of product management experience owning complex B2B SaaS or AI-enabled platforms.
• Experience as principal PM, staff PM, or senior IC responsible for platform-level outcomes.
• Experience defining strategy across multiple teams and delivering commercial outcomes.

Aufgaben

• Define and drive the long-term product vision, strategy, roadmap, and platform architecture for HighLevel Knowledge Bases and Ask AI.
• Own the complete Ask AI experience, including chat, voice interaction, conversation history, memory, templates, artifacts, tasks, tools, actions, agent routing, and lifecycle management.
• Own the Knowledge Base platform across ingestion, parsing, embedding, indexing, retrieval, and observability.
• Establish product principles for how Ask AI answers, retrieves knowledge, generates plans, invokes tools, and handles failures.

Jobbeschreibung

We are hiring a Principal Product Manager to own the long-term product strategy for this intelligence and assistance platform. This person will define the architecture, product principles, quality systems, developer model, governance, metrics, and commercial strategy required to make Ask AI the default way users understand and operate HighLevel.

This is not a generic chatbot, enterprise search, or content-generation role. It is a senior individual-contributor product leadership role at the intersection of AI assistants, enterprise knowledge, retrieval systems, agent orchestration, workflow execution, CRM data, permissions, automation, developer platforms, and multi-tenant SaaS. You will influence multiple product and engineering teams, establish shared platform standards, and make foundational decisions that affect every AI product across HighLevel.

Responsibilities:

• Define and drive the long-term product vision, strategy, roadmap, and platform architecture for HighLevel Knowledge Bases and Ask AI.
• Own the complete Ask AI experience, including chat, voice interaction, conversation history, memory, templates, artifacts, scheduled tasks, tools, actions, agent routing, browser interaction, feedback, permissions, approvals, auditability, usage, and lifecycle management.
• Own the Knowledge Base platform across source ingestion, extraction, parsing, chunking, embeddings, indexing, metadata, retrieval, re-ranking, citations, freshness, versioning, testing, permissions, and observability.
• Establish the product principles that determine how Ask AI answers questions, requests more information, retrieves knowledge, generates a plan, selects a tool, invokes an agent, asks for approval, executes an action, handles failure, and communicates results.
• Create a shared context model spanning user identity, agency, location, role, permissions, current interface, business profile, brand voice, memory, knowledge bases, CRM data, product configuration, and previous tool outputs.
• Develop a deep understanding of agency owners, marketers, sales teams, customer-service teams, operations leaders, administrators, and SMB employees who use HighLevel to perform daily work.
• Personally inspect Ask AI conversations, tool traces, knowledge base retrieval results, user feedback, support tickets, source ingestion failures, incorrect answers, failed actions, and downstream customer outcomes.
• Partner closely with AI and engineering leadership on LLM orchestration, model routing, context management, prompt systems, tool invocation, agent planning, memory, retrieval-augmented generation, embeddings, hybrid search, re-ranking, caching, latency, evaluation, observability, and cost.
• Partner with platform and infrastructure teams on ingestion pipelines, crawlers, document processing, vector storage, structured-data retrieval, indexing, refresh jobs, multi-tenant isolation, scalability, reliability, and data retention.
• Define an evaluation architecture covering Ask for AI responses, knowledge base retrieval, memory usage, tool selection, action accuracy, permissions, artifacts, latency, and cost.
• Build representative test datasets from real agency and SMB use cases across industries, account types, languages, data structures, and product workflows.
• Partner with HighLevel product teams to expose their capabilities safely through Ask AI using shared action, permission, approval, error, and result contracts.
• Establish a scalable onboarding model for product teams that want to contribute Ask AI actions, templates, skills, artifacts, or specialized agents.
• Advance integration between Ask AI and Agent Studio so custom agents can be discovered, selected, invoked, monitored, and improved from a unified Ask AI experience.
• Define the platform model for MCP tools, native product actions, external APIs, web search, browser execution, knowledge base retrieval, and other agent capabilities.
• Build robust observability and debugging experiences that show selected context, retrieved sources, model outputs, plans, tools, inputs, permissions, approvals, action results, errors, latency, and usage.
• Improve Knowledge Base quality-management workflows, including retrieval testing, source inspection, stale-content detection, crawl diagnostics, document-processing errors, conflict detection, and customer-facing recommendations.
• Define the architecture for reusable, inherited, bundled, and marketplace-distributed knowledge bases across agencies and locations.
• Partner with Design to simplify knowledge base creation, source addition, testing, maintenance, agent attachment, Ask AI onboarding, template discovery, approval flows, artifact interaction, and error recovery.
• Develop a coherent experience across Ask AI templates, open-ended prompting, guided questions, native skills, specialized agents, and scheduled tasks.
• Define instrumentation across the full funnel, including Ask AI discovery, first prompt, first useful response, first successful action, repeat usage, template completion, scheduled-task retention, knowledge base creation, source ingestion, retrieval tests, agent attachment, and sustained production usage.
• Define quality and outcome metrics by request type rather than treating every Ask AI interaction as equivalent.
• Partner with Security, Legal, Privacy, and Trust teams on data isolation, access control, memory, browser automation, sensitive data, source permissions, content retention, external-model processing, audit logs, and abuse prevention.
• Establish standards for destructive actions, bulk changes, financial operations, external communication, and compliance-sensitive workflows.
• Partner with Product Marketing on positioning, use-case packaging, customer education, competitive differentiation, launches, templates, and agency enablement.
• Partner with Support, Implementation, Trial Experience, Account Management, Affiliates, and internal operations teams to reduce setup friction, improve outcomes, and lower support burden.
• Partner with Finance and Revenue Experience in packaging, pay-per-use pricing, AI employee plans, agency rebilling, cost controls, gross margin, and commercial limits.
• Develop a clear competitive understanding of AI copilots, enterprise search, AI workspaces, CRM assistants, agent platforms, browser agents, knowledge-management products, and workflow-automation tools.
• Evaluate external model providers, search and retrieval technologies, document-processing systems, vector databases, re-ranking systems, browser technologies, and strategic partnerships.
• Influence strategy across Conversation AI, Voice AI, Agent Studio, Workflow AI, CRM, automation, communications, commerce, and the broader HighLevel platform.
• Act as a senior product thought partner to Product, Engineering, Design, Data, Security, GTM, Finance, and executive leadership.
• Mentor PMs and raise the quality of AI product strategy, platform thinking, evaluation, and decision-making across the organization without relying on formal authority.

Requirements:

• 12+ years of product management experience owning complex B2B SaaS, AI-assisted, enterprise-search, knowledge-management, workflow-automation, CRM, developer-platform, or AI-agent products. Scope and demonstrated outcomes matter more than a precise number of years.
• Prior experience operating as a principal PM, staff PM, lead PM, product founder, or equivalent senior individual contributor responsible for a business-critical platform.
• Evidence of defining strategy across multiple teams and delivering sustained customer and commercial outcomes, not merely shipping individual AI features.
• Experience building production AI products that combine answers, context, tools, workflows, and actions.
• Strong understanding of the distinction between language generation and grounded answers, retrieval quality and final-answer quality, a chat interface, and an agentic operating system, A successful demonstration and a dependable production product, model intelligence, and complete system reliability
• Technical fluency across several of the following: retrieval-augmented generation, document ingestion and parsing, chunking strategies, embeddings and vector search, keyword and hybrid retrieval, metadata filtering and query rewriting, re-ranking and source attribution, and structured-data retrieval.
• Knowledge freshness and lifecycle management, LLM orchestration, tool and function calling, agent routing, context management and memory systems, evaluation and observability, and APIs and webhooks.
• Strong judgment around AI evaluation, able to define how retrieval, answers, plans, tools, actions, artifacts, and memory should be tested and monitored.
• Strong systems thinking able to connect identity, permissions, agency, location context, CRM data, knowledge bases, memory, tools, workflows, billing, and reporting into a coherent platform.
• Experience designing products where AI can change business data or execute consequential actions.
• Strong instincts around permissions, approvals, auditability, reversibility, sensitive information, and customer trust.
• Experience simplifying technically sophisticated systems for non-technical users.
• Experience with multi-tenant SaaS and the operational needs of agencies, resellers, franchises, enterprises, or multi-location businesses.