Product Manager

Job Description

MyOperator is building AI-driven conversational surfaces — chatbots and voicebots that handle real business conversations, run structured journeys, pull from knowledge bases, and hand off to human agents when needed. We're looking for a Product Manager to own this domain: someone who genuinely understands how LLMs and RAG (Retrieval-Augmented Generation) systems work, and can turn that understanding into product decisions — what to build, which model tradeoffs to make, and how to know if a chatbot or voicebot is actually working.

This is a technical AI product role. You'll be defining journeys, knowledge base behavior, and evaluation criteria for systems where outputs are probabilistic, not deterministic — so the ability to reason about model behavior, failure modes, and retrieval quality matters as much as classic product skills.

About MyOperator

MyOperator is a Business AI Operator platform that allows businesses, teams, and AI Agents to work in tandem for customer operations, i.e., handle Sales, Support, Escalation, Feedback, and Refund processes. With over 12,000+ businesses using our platform, we are the largest in the space.

MyOperator is built for people who want to work on ambitious problems at a meaningful scale. We value ownership, speed, critical thinking, and a bias for building things that create real customer and business outcomes. This is a high-expectation, high-learning environment where people are trusted to think independently, challenge ideas openly, move with urgency, and keep raising the bar as we build for long-term impact.

Key Responsibility Areas

• Own the conversational AI product roadmap — chatbot and voicebot journeys, knowledge base/RAG behavior, and model selection — writing specs that account for real LLM behavior and failure modes, not just ideal-case flows.
• Define and maintain how success is measured — partner with engineering to build and evolve eval systems that track quality (accuracy, containment, hallucination rate) so every release has a clear bar to clear.

Requirements — Must Have

• Strong practical understanding of how LLMs work — prompting, context windows, tool/function calling, and their real-world limitations in production systems.
• Solid understanding of RAG (Retrieval-Augmented Generation) systems — how retrieval, embeddings, and knowledge bases feed into model responses, and where these systems typically break down.
• Working knowledge of the AI model landscape — tradeoffs between models on cost, latency, and accuracy — and how to translate that into build/model-selection decisions for a product.
• Demonstrated ability to write specs for AI-driven product surfaces (chatbot/voicebot journeys, escalation/handover logic) that explicitly account for probabilistic model behavior and edge cases.
• 4+ years as a Product Manager, with meaningful hands-on time owning an AI/LLM-powered product end to end.

Requirements — Good to Have

• Hands-on background building or shipping chatbots or voicebots — design, integrations, and deployment.
• Experience designing conversational journeys — structured, multi-step dialogue or task flows for chat or voice agents.
• Experience building or defining eval systems/frameworks to measure chatbot or voicebot success (e.g., accuracy, containment rate, hallucination rate).
• Familiarity with prompt engineering and iterating on prompts/journeys based on eval results.
• Experience designing human-handover or escalation flows between a bot and a live agent.

This profile is not for

• Candidates who treat "AI feature" as a checkbox without understanding how LLMs and RAG systems actually behave in production, including their failure modes.
• Candidates focused purely on traditional, deterministic software product work with no interest in model behavior, prompt design, or evaluation methodology.
• Candidates uncomfortable with ambiguity — AI product behavior is probabilistic, and specs need to account for that rather than assume fixed outcomes.

Skills

Chatbot, Voicebot, Large Language Models (LLM) tuning, Retrieval Augmented Generation (RAG), Artificial Intelligence (AI)