Principal Product Manager - Knowledge Bases & Ask AI
HighLevel · India
About this role
The Principal Product Manager for Knowledge Bases & Ask AI will lead the strategy and execution of AI-driven product features at HighLevel. This role focuses on building and scaling intelligent knowledge management and conversational AI capabilities. Responsibilities: Lead the end-to-end product lifecycle for AI features, specifically focusing on Knowledge Bases and Ask AI functionality. Drive the development of products utilizing LLMs, RAG, and AI agents to enhance user experience. Collaborate with engineering teams to integrate complex AI solutions with existing platform infrastructure. Manage API and webhook integrations to ensure seamless connectivity within the developer ecosystem. Requirements: Proven experience in taking an AI product from concept to production. Demonstrated expertise in building products using LLMs, RAG, or AI agents. Strong technical understanding of APIs, webhooks, and developer platforms. Ability to articulate product vision and execute complex technical roadmaps. This is a remote position based in India.
For full details about this role, click “Apply on company site” to view the employer’s original posting.
A focused preparation guide, built around this job’s responsibilities and requirements.
✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
Understand the role
What matters most
AI Feature Lifecycle Management
The core priority is leading the end-to-end product lifecycle for Knowledge Bases and Ask AI. You must demonstrate the ability to translate high-level AI strategy into production-ready features, specifically leveraging LLMs, RAG, and AI agents to solve user needs.
Technical Integration and Ecosystem Connectivity
You are expected to bridge the gap between complex AI models and the existing platform infrastructure. This requires deep proficiency in managing API and webhook integrations to ensure that AI capabilities are seamlessly accessible within the broader developer ecosystem.
Technical Roadmap Execution
As a Principal PM, you must articulate a clear product vision and execute complex technical roadmaps. This involves collaborating closely with engineering teams to balance rapid AI innovation with the stability and scalability requirements of the HighLevel platform.
Likely challenges to think through
Balancing AI Performance with Platform Stability
Integrating LLMs and RAG into an existing platform often introduces latency and reliability trade-offs. Prepare to discuss how you manage the tension between deploying cutting-edge AI features and maintaining the performance standards of the core product infrastructure.
Managing Developer-Facing AI Complexity
Since the role involves API and webhook management, you will likely face the challenge of abstracting complex AI logic into simple, usable interfaces for developers. Prepare by reviewing strategies for designing developer-centric AI products that remain robust and easy to integrate.
03
Practice your answers
Technical practice
LLM Integration Strategy
What to coverExplain your approach to selecting and integrating LLMs for a knowledge base product. Discuss how you evaluate trade-offs between model performance, latency, cost, and data privacy requirements.
RAG Architecture Implementation
What to coverDescribe the technical components required for a robust Retrieval-Augmented Generation (RAG) pipeline. Focus on how you handle document ingestion, vector database selection, and retrieval accuracy to minimize hallucinations.
AI Agent Orchestration
What to coverHow do you design and manage autonomous AI agents within a platform ecosystem? Discuss the challenges of maintaining state, managing tool-use capabilities, and ensuring reliable agent behavior.
API and Webhook Design for AI
What to coverDetail your experience in building developer-facing APIs and webhooks for AI products. Explain how you ensure scalability, security, and ease of integration for third-party developers.
Managing AI Product Lifecycle
What to coverDescribe your process for taking an AI feature from initial concept to production. Suggestion: Focus on how you define success metrics, manage model evaluation, and handle iterative feedback loops.
Technical Roadmap Execution
What to coverHow do you balance the need for rapid AI innovation with the stability of existing platform infrastructure? Discuss your methodology for prioritizing technical debt versus new feature development.
Behavioural practice
Navigating AI Product Ambiguity
What to coverDescribe a situation where you had to define a product vision for an emerging AI technology with significant uncertainty. Explain how you aligned stakeholders and executed the roadmap.
Cross-functional Collaboration
What to coverProvide an example of how you collaborated with engineering teams to integrate complex AI solutions into a legacy platform. Highlight how you resolved technical disagreements or resource constraints.
Handling AI Product Failure
What to coverTell me about a time an AI feature did not perform as expected in production. Describe the actions you took to diagnose the issue, communicate with stakeholders, and pivot the strategy.
Managing Developer Ecosystems
What to coverDescribe a time you had to manage the needs of a developer community while building a platform product. Explain how you balanced developer experience with the core product strategy.
02
Sharpen your knowledge
Topics to prepare
Retrieval-Augmented Generation (RAG) Architecture
Review the conceptual flow of RAG, specifically how document retrieval interacts with LLM generation to reduce hallucinations. Exercise: Map out a high-level architecture diagram for a Knowledge Base system that handles user queries, retrieves relevant chunks, and generates context-aware responses, as this is central to the Ask AI functionality.
AI Agent Lifecycle Management
Understand the stages of moving an AI agent from prototype to production, including evaluation metrics and feedback loops. Exercise: Define a set of KPIs for measuring the success of an AI agent, such as response accuracy, latency, and user engagement, to demonstrate your ability to manage the product lifecycle.
Skills to revise
API and Webhook Integration Strategy
Practice designing robust API-first workflows for developer platforms. Exercise: Draft a technical specification for a webhook-based notification system that triggers an AI agent action, ensuring you can explain how to handle authentication, rate limiting, and error handling in a production environment.
LLM Product Roadmap Execution
Focus on translating complex technical requirements into actionable product roadmaps. Exercise: Create a mock roadmap for a new feature that integrates an LLM into a knowledge base, prioritizing tasks based on technical feasibility and user impact to show your ability to lead execution.
04
Your preparation checklist
Prepare a detailed AI product case study
Draft a structured narrative of an AI product you led from concept to production. Focus on the specific challenges of implementing LLMs or RAG, the metrics used to measure success, and how you navigated technical constraints during the development lifecycle.
Map your RAG and AI agent experience
Create a technical summary of your experience building with LLMs, RAG, or AI agents. Be prepared to explain your architectural choices, how you handled data retrieval accuracy, and how you managed the trade-offs between latency and model performance.
Document your API and developer platform expertise
Prepare examples of how you have managed API-first products or developer ecosystems. Highlight specific instances where you utilized webhooks to enable third-party integrations and how you designed these interfaces to be scalable and developer-friendly.
Formulate strategic questions for the interview
Develop thoughtful questions regarding HighLevel's current knowledge management infrastructure, the primary user pain points for the Ask AI feature, and how the team balances rapid AI feature deployment with platform stability and security.
Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy
Share this opportunityKnow someone who would be a great fit?