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AI Engineer at Willis Limited, trading as Willis Towers Watson plc 
Hong Kong, Hong Kong Island, Hong Kong S.A.R. -
Full Time

Start Date

Immediate

Expiry Date

04 May, 26

Salary

0.0

Posted On

03 Feb, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

AI Engineering, LLM Fine-tuning, LLM Deployment, Large-scale Datasets, Specialized Servers, Retrieval-Augmented Generation, RAG, Multi-agent Systems, Multimodal LLMs, Backend APIs, Data Processing, Data Mining Pipelines, Machine Learning Models, MLOps, CI/CD Pipelines, Quantitative Investment

Industry

Financial Services

Description
Key Responsibilities Design, implement, and deploy AI-driven applications Fine-tuning and deployment of LLMs, ensuring integration with large-scale, complex datasets. Develop and integrate specialized servers (e.g., MCP) tailored to industry-specific data such as insurance claims, premium records, customer risk scores, and more. Leverage advanced AI/LLM techniques such as Retrieval-Augmented Generation (RAG), multi-agent systems, multimodal LLMs, and MCP platforms to deliver innovative features and business value. Develop and maintain backend APIs ensuring high efficiency, scalability, and reliability. Prepare and process complex datasets for effective AI integration, ensuring compliance with data privacy and regulatory requirements. Build data-mining pipelines for large-scale databases and implement machine learning models. Apply MLOps principles, including CI/CD pipelines, to streamline AI development and deployment workflows. Conduct research on LLM and multi-agent system applications in quantitative investment and cryptocurrency markets.

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Responsibilities
The role involves designing, implementing, and deploying AI-driven applications, focusing heavily on fine-tuning and deploying Large Language Models (LLMs) integrated with complex, large-scale datasets. Responsibilities also include developing specialized servers for industry-specific data and leveraging advanced AI techniques like RAG and multi-agent systems.