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LVMH Louis Vuitton Moet Hennessy · Chinese Mainland

# Data & AI tech Manager/Data & AI Tech Manager

Chinese Mainland Manager or lead To be negotiated Data & Analytics 

Listed on LVMH Louis Vuitton Moet Hennessy’s official careers site. Posted March 24, 2026. Check that it is still open and apply there.
[Apply on LVMH Louis Vuitton Moet Hennessy’s careers site →](https://www.lvmh.com/en/join-us/our-job-offers/TP03220)

[OpenNash Jobs](https://jobs.opennash.com/) / [LVMH Louis Vuitton Moet Hennessy](https://jobs.opennash.com/companies/lvmh-louis-vuitton-moet-hennessy/) / Data & AI tech Manager/Data & AI Tech Manager

## Job description

### Position Principal Responsibilities

Data Management and Extraction
- Maintain an inventory of data assets, including databases, data sources, and formats, ensuring that all data is easily accessible and well-organized.
- Design and implement processes for data extraction from various internal and external sources, ensuring accurate and timely retrieval of information.

### Data Integration
- Collaborate with other functions to design and implement system integration solutions that ensure seamless data flow between various applications and platforms.
- Manage and optimize databases to ensure efficient data storage, retrieval, and processing, supporting the organization’s data needs.

### Data Analytics and Tools
- Collaborate with business units to gather data requirements, providing support for data analysis via reporting tools, system integration, and AI applications.
- Work closely with various departments to understand their data needs and provide support for data-related projects and initiatives.
- Oversee the selection, implementation, and management of data analytical tools and platforms (e.g., BI tools, data visualization software) to enhance data analysis capabilities across the organization. Agentic Architecture & System Design

### Design Multi-Agent Systems
- Architect scalable multi-agent orchestration frameworks where specialized agents collaborate to solve complex, multi-step business problems.

### Build Advanced RAG Pipelines
- Lead the development of next-gen Retrieval-Augmented Generation (RAG) systems that go beyond simple vector search, integrating Knowledge Graphs, hybrid search, and re-ranking models to provide agents with precise, context-aware grounding.

### Memory & State Management
- Design sophisticated long-term and short-term memory mechanisms (using vector DBs, SQL, or key-value stores) to allow agents to maintain context across long-running sessions and learn from past interactions. Application Engineering & Product Delivery

### End-to-End Agent Lifecycle
- Own the full software development lifecycle (SDLC) for AI applications, from prototype to production, ensuring agents are not just demos but reliable, latency-optimized products.

### Human-in-the-Loop (HITL) Interfaces
- Build intuitive UI/UX patterns for human-agent collaboration, including approval gates, intervention points, and feedback loops humans guide or correct agent actions before final execution.

### Evaluation & Testing Frameworks
- Establish rigorous automated testing suits for agents to measure success rates, task completion accuracy, hallucination frequency, and tool invocation reliability.

### Performance Optimization
- Optimize application performance by implementing caching strategies, prompt compression, model routing, and asynchronous processing to reduce latency and token costs. Reliability, Safety & Governance (Agent-Specific)

### Guardrails & Safety Layers
- Implement advanced guardrail systems to prevent agents from taking unauthorized actions, accessing sensitive data, or entering infinite loops. This includes input/output filtering and constraint enforcement.

### Deterministic Workflow Enforcement
- Balance probabilistic LLM reasoning with deterministic workflow engines to ensure critical business processes remain predictable and auditable.

### Observability & Debugging
- Deploy comprehensive observability stacks specifically for agentic flows, tracing decision paths, tool calls, and reasoning steps to rapidly debug failures in complex autonomous chains.

### Cost & Token Governance
- Monitor and optimize token consumption and compute costs associated with high-frequency agent operations, implementing budget limits and efficiency protocols. Technical Leadership & Innovation

### Framework Selection & Strategy
- Evaluate and select the best open-source and proprietary agent frameworks and define the team’s technical stack.

### POC to Production Pipeline
- Rapidly prototype new agent concepts and define the clear criteria and engineering standards required to graduate them into mission-critical production applications.

### Cross-Functional Integration
- Collaborate closely with product managers and domain experts to translate vague business needs into structured agent specifications and actionable user stories.

### Team Upskilling
- Mentor engineers on prompt engineering patterns, chain-of-thought reasoning, few-shot learning, and the nuances of building non-deterministic software systems. ------------------------------------------- Principal Responsibilities Data Management and Extraction

### Team Upskilling
- Mentor engineers on prompt engineering patterns, chain-of-thought reasoning, few-shot learning, and the nuances of building non-deterministic software systems.

## At a glance
- Posted by the employer: Mar 24, 2026
- First seen by OpenNash: Jul 19, 2026
- Last checked: Oct 11, 2026
- Listed for: 201 days
- Location: Chinese Mainland
- Pay: To be negotiated[Apply on LVMH Louis Vuitton Moet Hennessy’s careers site →](https://www.lvmh.com/en/join-us/our-job-offers/TP03220)

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