工作描述
7 天前
Location: Hong Kong
Salary: HKD 30,000 - 50,000 per month (Negotiable based on experience)
Tenure: Extendable Contract
Our client is a global retail powerhouse and a household name with a massive physical and digital footprint across Asia and Europe. As one of the world's largest international health and beauty retailers, they are currently undergoing an ambitious AI-led transformation.
The successful candidate will join their specialized Digital/Innovation hub. This team is tasked with integrating Large Language Models (LLMs) into a vast ecosystem that serves millions of customers daily. Unlike typical research roles, you will be building production-grade AI -developing intelligent chatbots, agentic workflows, and sophisticated RAG systems that directly impact global supply chains, personalized marketing, and customer engagement. You will be working with massive, high-quality datasets that provide a unique "playground" for fine-tuning and optimizing LLM performance at scale.
Key Responsibilities
• Agentic Systems: Design and develop LLM-powered applications, focusing on multi-agent systems, prompt orchestration, and complex tool/function calling for multi-step task coordination.
• Advanced RAG: Build and optimize Retrieval-Augmented Generation (RAG) capabilities, including data ingestion, chunking strategies, vector search, reranking, and citation attribution.
• Data Engineering: Maintain data pipelines to curate external knowledge sources, ensuring freshness, quality, and strict access control for LLM consumption.
• Optimization: Improve system performance via prompt engineering, dataset curation, and lightweight adaptation techniques (e.g., LoRA/PEFT).
• LLMOps: Implement deployment, monitoring, and evaluation frameworks to ensure production quality, low latency, and cost-efficiency.
• Collaboration: Work alongside ML Engineers and Data Scientists to integrate AI solutions into existing APIs and downstream platforms.
Required Qualifications
• Education: Degree in Computer Science, Mathematics, Data Science, or a related field.
• Technical Skills: Strong proficiency in Python with experience building production-grade services (APIs, CI/CD, Async jobs).
• AI Frameworks: Hands-on experience with LangChain, LangGraph, or LlamaIndex .
• LLM Delivery: Experience developing LLM applications and improving them through systematic experimentation (RAG tuning, dataset iteration, or fine-tuning).
• Evaluation Mindset: Ability to define success metrics (groundedness, faithfulness, retrieval metrics) and run A/B tests or human reviews.
• Production Experience: Experience deploying models in containerized environments or cloud-native scalable architectures.
• Responsible AI: Familiarity with AI security, including prompt injection defense, privacy, and guardrails.
Preferred Skills (Nice to Have)
• Experience with Kubernetes or container orchestration.
• Familiarity with observability stacks (OpenTelemetry, distributed tracing, and structured logging).
Argyll Scott Asia is acting as an Employment Business in relation to this vacancy.
Salary: HKD 30,000 - 50,000 per month (Negotiable based on experience)
Tenure: Extendable Contract
Our client is a global retail powerhouse and a household name with a massive physical and digital footprint across Asia and Europe. As one of the world's largest international health and beauty retailers, they are currently undergoing an ambitious AI-led transformation.
The successful candidate will join their specialized Digital/Innovation hub. This team is tasked with integrating Large Language Models (LLMs) into a vast ecosystem that serves millions of customers daily. Unlike typical research roles, you will be building production-grade AI -developing intelligent chatbots, agentic workflows, and sophisticated RAG systems that directly impact global supply chains, personalized marketing, and customer engagement. You will be working with massive, high-quality datasets that provide a unique "playground" for fine-tuning and optimizing LLM performance at scale.
Key Responsibilities
• Agentic Systems: Design and develop LLM-powered applications, focusing on multi-agent systems, prompt orchestration, and complex tool/function calling for multi-step task coordination.
• Advanced RAG: Build and optimize Retrieval-Augmented Generation (RAG) capabilities, including data ingestion, chunking strategies, vector search, reranking, and citation attribution.
• Data Engineering: Maintain data pipelines to curate external knowledge sources, ensuring freshness, quality, and strict access control for LLM consumption.
• Optimization: Improve system performance via prompt engineering, dataset curation, and lightweight adaptation techniques (e.g., LoRA/PEFT).
• LLMOps: Implement deployment, monitoring, and evaluation frameworks to ensure production quality, low latency, and cost-efficiency.
• Collaboration: Work alongside ML Engineers and Data Scientists to integrate AI solutions into existing APIs and downstream platforms.
Required Qualifications
• Education: Degree in Computer Science, Mathematics, Data Science, or a related field.
• Technical Skills: Strong proficiency in Python with experience building production-grade services (APIs, CI/CD, Async jobs).
• AI Frameworks: Hands-on experience with LangChain, LangGraph, or LlamaIndex .
• LLM Delivery: Experience developing LLM applications and improving them through systematic experimentation (RAG tuning, dataset iteration, or fine-tuning).
• Evaluation Mindset: Ability to define success metrics (groundedness, faithfulness, retrieval metrics) and run A/B tests or human reviews.
• Production Experience: Experience deploying models in containerized environments or cloud-native scalable architectures.
• Responsible AI: Familiarity with AI security, including prompt injection defense, privacy, and guardrails.
Preferred Skills (Nice to Have)
• Experience with Kubernetes or container orchestration.
• Familiarity with observability stacks (OpenTelemetry, distributed tracing, and structured logging).
Argyll Scott Asia is acting as an Employment Business in relation to this vacancy.

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