- Location
- Quarry bay, Hong Kong Island
- Type
- Full-time
- Department
- Education
- Experience
- 2+ years
- Education
- Master
- Closing date
- Today
- Source
- Vincere
Description
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.
- Perform prompt tuning upon use of large language model (LLM) and any other foundation models to achieve requirements
- Collaborate with software engineers and solution architects 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
- Bachelor's or Master's degree in Computer Science, Data Science, Electrical Engineering, or a related field.
- 2+ years of professional experience in Data Science and Machine Learning or enterprise application development
- AI & ML Development: Building and maintaining machine learning models for NLP and computer vision.
- Retrieval-Augmented Generation (RAG): Expertise in designing and optimizing RAG pipelines using Azure AI Search or similar tools.
- Agentic AI Workflows: Implementing autonomous agents with multi-step reasoning with large language model (LLM) and tool integration.
- Model Context Protocol (MCP): Understanding and prototyping interoperability concepts.
- Performance Optimization: A/B testing, tuning, and monitoring AI systems in production.
- Containerized Deployment: Experience with OpenShift or Kubernetes for cloud-native applications.
- Python Programming: Scripting, automation, and model development.
Skills
PythonAzureKubernetesMachine LearningNLPComputer VisionData SciencePrototyping
