ICHEERZI (Hong Kong) LIMITED
AI Engineer
Skills
12 days ago
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Important information
Contract type:
Permanent
Work mode:
Onsite
Published on:
12 October 2026
What they need
Job Responsibilities
Develop and maintain AI application and ML models for tasks including natural language processing, computer vision ensuring high performance and scalability, with a strong focus on building and optimizing Retrieval-Augmented Generation (RAG) applications using Azure AI Search or related tools for efficient information retrieval and indexing.
Research, design, and implement agentic AI workflows, enabling autonomous agents to handle complex, multi-step tasks through tool integration and decision-making logic.
Explore and prototype latest AI and ML concepts, such as the Model Context Protocol (MCP), to enhance application interoperability and functionality.
Conduct performance tuning, A/B testing, and monitoring of AI and ML systems in production environments, deploying solutions on OpenShift for containerized orchestration.
Collaborate with software engineers to embed AI and ML capabilities into web /mobile applications, using Python for scripting, automation, and model development in prototype level
Document technical designs, contribute to code reviews, and maintain best practices in AI and ML.
Requirements / Qualifications
Bachelor's or Master's degree in Computer Science, Data Science, Electrical Engineering, or a related field.
1+ years of professional experience in Data Science and Machine Learning or enterprise application development
Experience in AI engineering or software development with AI components is advantage.
Strong experience with cloud platforms (AWS, GCP, or Azure) for deploying AI and ML workloads, with specific hands-on knowledge of Azure AI Search and Copilot Studio for RAG and agentic implementations is a plus
Excellent problem-solving skills and ability to work in agile environments.
Strong communication skills, with the ability to explain complex AI and ML concepts to non-technical stakeholders.
Experience with vector databases or knowledge graphs for RAG is a plus
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