CEO Office AI Solutions Engineer - Production Tools

The Caravel Group

Hong Kong

On-site

HKD 1,000,000 - 2,000,000

Full time

8 days ago

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Job summary

The Caravel Group in Hong Kong is scaling AI as a core capability, treating it as mission-critical software: a highly autonomous, hands-on role in the Office of the Group CEO focused on practical AI tools that improve executive productivity and decision speed.

As an AI Solutions Engineer, Office of the Group CEO, you will turn ambiguous executive problems into dependable software, AI-enabled workflows and decision-support tools, and collaborate with Fleet Management Limited Technology to align

Qualifications

6+ years of hands-on experience as a software, platform, AI or data engineer, with evidence of delivering production-grade tools.
Hands-on experience with AI / LLM capabilities, including deploying and operating them in real systems, with familiarity in agent frameworks, RAG, vector stores and tool orchestration.
Strong experience building APIs, backend services and data pipelines across documents, analytics platforms, AI services and internal or external data sources.
Practical experience using analytics and light machine-learning techniques where useful, with strong Python skills for backend services, automation and data handling.
Practical cloud engineering experience (AWS, Azure or Google Cloud), including deploying and operating services in production.
Strong grounding in modern engineering practices including CI/CD, testing, version control, infrastructure automation and core MLOps / LLMOps disciplines.
Ability to build clear C-suite visualisations and interfaces that support real decision-making.
Experience designing systems for reliability, scalability, security and operational support when handling sensitive commercial or operational data.
Degree in Computer Science, Engineering, Data, Mathematics or a similar technical discipline

Responsibilities

Design and build autonomous agents and data pipelines that pull, structure and synthesise information from internal systems.
Build executive dashboards, reports and tools that combine data across functions and provide clear, low-latency insight for decision-making.
Design, build and maintain backend services, data pipelines and platform components that support AI workloads at scale.
Apply practical analytics, forecasting and light machine learning where they materially improve decision quality.
Implement shared platform capabilities such as environment provisioning, configuration, secrets management and deployment pipelines.
Apply strong engineering practices including version control, testing, CI/CD and clear documentation.
Ensure solutions align with FML technology standards, security requirements and architectural guardrails.