AI Product Operations Manager

Role Overview

We are looking for an experienced AI Product Operations Manager to lead end-to-end product operations for our global AIOS (AI Operating System). In this role, you will focus on large language model (LLM) capability iterations and consumer-facing AI user experiences across smart screen and home intelligence environments. You will turn deep regional user insights across key markets into actionable strategies for ambient intelligence, personalized home services, and LLM quality governance.

Key Responsibilities

• User Insights & Strategy: Research global user interaction habits, household device usage, and scenario needs across Europe, America, Southeast Asia, and India. Track competitor capabilities (Google TV, Roku, Amazon) to identify demands for wake-free interaction, multi-turn dialogue, and on-device offline AI.

• Core C-End AI Experience Operations: Manage operations for AI Homepage, ambient proactive intelligence, and personalized recommendations. Define scenario trigger rules, delivery scheduling, and contextual adaptation to evolve AI from passive Q&A into an immersive home assistant.

• LLM Quality & Capability Governance: Manage corpus collection, annotation standards, model evaluations, prompt engineering, and hallucination mitigation for cloud-edge integrated models. Partner with algorithm and product teams to optimize multi-lingual recognition accuracy, conversation coherence, and decision quality.

• Data-Driven Iteration: Use product metrics, user behavior, AI conversation logs, and negative feedback to locate bottlenecks. Drive continuous product improvements and increase AI feature penetration, user retention, and satisfaction via A/B testing.

Key Requirements

• Education & Experience: Bachelor’s degree or higher with 3+ years of experience in AI product operations, LLM experience operations, or international product operations.

• Domain Knowledge: Strong understanding of LLM fundamentals, multi-turn interaction, personalized recommendations, and scene-based intelligence. Ability to diagnose model output issues and dissect UX gaps.

• Data & Analytics: Solid analytical skills (root-cause analysis, data decomposition) with practical experience using A/B testing to drive algorithm and UX improvements.

• Language & Communication: Fluent in English (written & spoken) for international team collaboration, user research, and competitive analysis.

Preferred Qualifications

• Hands-on experience with large-screen hardware, smart home systems, or cloud-edge collaborative AI.

• Practical exposure to LLM evaluation, prompt engineering, corpus operations, or AI Agent implementations.

• Familiarity with overseas smart device ecosystems (Google TV, Samsung Tizen, Amazon Fire TV).