Purpose of the Position:
As an AI Engineer (Internal Automation), you will be a key driver in transforming our internal business operations into an AI-first ecosystem. Moving beyond theoretical models, your focus will be on the pragmatic, high-impact application of Generative AI, Large Language Models (LLMs), and intelligent workflows to streamline operations across business units.
You will own the complete technical lifecycle from inception to enablement—partnering with cross-functional business units to gather business requirements, building lightweight prototypes, and leading user-enablement initiatives. Working closely with senior AI teammates, you will actively identify operational needs and research emerging AI technologies to architect solutions that maximize efficiency, sharpen enterprise decision-making, and elevate the internal user experience.
In this role, you will:
1. Requirement Gathering & Operational Discovery
- Cross-Functional Consulting: Partner with internal department stakeholders to understand their operations needs and identify suitable AI or automation use cases.
- Technical Scoping: Translate broad business needs into crisp, technical AI specifications, aligning expectations with stakeholders.
2. Agile Prototyping & AI Engineering
- Lightweight Prototyping: Develop, test, and iterate proof-of-concepts (PoCs) using LLMs, multi-agent orchestration frameworks, and Retrieval-Augmented Generation (RAG) such as LangChain, LlamaIndex, CrewAI, Semantic Kernel, or vLLM to validate business value before full-scale deployment.
- Systems Integration: Seamlessly connect AI solutions with enterprise systems, databases, and third-party APIs using Python and integration middleware.
3. User Enablement & Change Management
- Adoption & Training: Lead internal user-enablement sessions, demonstrations, and prepare user guides to ensure non-technical staff can adopt new AI tools.
- Feedback Loops: Continuous monitoring and gathering feedback with internal users to refine AI assistants and improvements.
4. Internal Knowledge-Building
- Knowledge Sharing: Conduct ongoing research AI technologies, tools, frameworks, and market trends, and summarize insights for internal reference.
Skills and Experience that will lead to success:
- Bachelor’s degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, or a highly quantitative/ technical discipline.
- Minimum of 2 years of experience in software engineering, data engineering, or systems automation, with a proven track record of building and deploying production-grade Generative AI applications (RAG, Agents).
- Hands-on experience working with LLM APIs (e.g., OpenAI, Anthropic, Gemini), fine-tuning prompt strategies, and utilizing AI development frameworks. Familiarity with AI coding assistants to optimize personal development velocity is a plus.
- Passionate about AI technologies, digital transformation, automation, and data‑driven problem solving
- Ability to bridge the gap between technical AI capabilities and non-technical business users
- Good analytical, problem-solving, communication, and coordination skills
- Willingness to learn new technologies and support digital/AI initiatives
- Good command of English and Chinese; Putonghua preferred
Work location will be at Causeway Bay office.
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