AI Engineer (Internal Platforms & Enablement)

Charlotte Frank; Hong Kong; On-site; HKD 500,000 - 700,000; Full time

Job summary

Leadingnation in Hong Kong is looking for an AI Engineer to drive the technical foundation of their AI ecosystem. You will architect robust AI infrastructure, ensuring the organization leverages cutting-edge technology and integrates AI capabilities into workflows.

Your role includes building a standardized 'Skills' library, conducting training programs, and monitoring AI advancements to maintain competitive advantage. A Bachelor's degree in a relevant field and 3–5 years of experience are required, along with strong programming skills in Python.

Qualifications

• 3–5 years of experience in AI engineering or software development.
• Strong programming skills in Python; knowledge of Java/Go is a plus.
• Hands-on experience with AI frameworks like PyTorch and TensorFlow.

Responsibilities

• Architect and maintain AI infrastructure for internal operations.
• Develop interactive platforms to integrate AI capabilities into workflows.
• Monitor AI landscape and evaluate new models and tools.

Skills

AI/ML framework experience

Python programming

Communication skills

Systems Thinking

Education

Bachelor's degree in relevant field

Tools

AWS

Docker

Kubernetes

Python

Job description

About the Role

We are on a mission to embed intelligence into every facet of our organization. We are looking for a seasoned AI Engineer to architect and drive the technical foundation of our internal AI ecosystem.

In this pivotal role, you will not only build robust AI infrastructure but also act as a catalyst for company-wide innovation.

You will be the technical backbone of our AI strategy—designing intelligent systems, standardizing cross-departmental workflows, and ensuring our organization leverages the most cutting‑edge tools available. This is not just a research role; it is a hands‑on engineering position for someone who thrives on building scalable solutions and empowering colleagues to harness the power of AI in their daily work.

Key Responsibilities

1. AI Infrastructure & Platform Engineering

• Architect & Deploy: Design, build, and maintain the core AI infrastructure that supports internal operations, from daily communications to complex project management.
• Multimedia Integration: Develop and operate interactive platforms (including web‑based tools and video systems) that seamlessly integrate AI capabilities into employee workflows, enhancing productivity and collaboration.
• Reliability & Scalability: Own the end‑to‑end performance of our AI systems, ensuring high availability, robust security, and the capacity to scale as our adoption grows. Implement comprehensive monitoring and proactive maintenance protocols.

2. Standardized Skills & Internal Enablement

• Develop a "Skills" Library: Collaborate with department heads (e.g., Marketing, HR, Sales) to identify pain points and opportunities. Build a standardized internal library of enterprise "Skills"—encompassing reusable prompts, automated workflows, autonomous agents, and custom toolkits tailored to specific business functions.
• Training & Upskilling: Design and deliver engaging internal training programs and workshops to elevate the practical AI literacy of our workforce. Transform complex technical capabilities into easy‑to‑understand, actionable skills.
• Documentation: Create and maintain clear, comprehensive documentation, best‑practice guides, and onboarding materials to ensure frictionless adoption across all teams.

3. Technology Tracking & Continuous Improvement

• Stay Ahead of the Curve: Actively monitor the rapidly evolving AI landscape. Evaluate new models, tools, and methodologies (e.g., LLMs, open‑source frameworks, MLOps tools) for their business relevance and potential impact.
• Strategic Implementation: Proactively recommend and lead the implementation of updates to our infrastructure, ensuring we remain at a competitive advantage.
• Cross‑functional Communication: Translate complex technical advancements into clear business value for both technical peers and non‑technical stakeholders, bridging the gap between innovation and practical application.

Requirements

Required

• Education: Bachelor’s degree or higher in Artificial Intelligence, Computer Science, Data Science, or a related technical field.
• Experience: 3–5 years of professional experience in AI engineering, software development, or a comparable technical role.

Technical Proficiency

• Strong programming skills (Python is required; experience with Java, Go, or similar is a plus).
• Hands‑on experience with AI/ML frameworks (e.g., PyTorch, TensorFlow), APIs, and integrating Large Language Models (OpenAI, Anthropic, open‑source models) into production environments.
• Proven experience building and maintaining web applications and/or multimedia platforms; familiarity with cloud infrastructure (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
• Systems Thinking: Deep understanding of AI integration, automation, and workflow design within a business context.
• Communication: Exceptional ability to distill complex technical concepts for non‑technical audiences and deliver effective training sessions.

Preferred

• Experience with vector databases, RAG (Retrieval‑Augmented Generation), and agentic frameworks (e.g., LangChain, AutoGen).
• Background in DevOps or MLOps practices, including CI/CD for machine learning models.
• Prior experience in an internal enablement, developer advocacy, or technical training role.