Exp in AI/Machine Learning architecture design
Attractive salary package and friendly culture
Work Life Balance work mode
One of our clients is an IT solution company in Hong Kong. They are now looking for an AI talents familiar with machine learning projects to join their professional AI team:
Collaborate with data scientists, data engineers, developers, operators (DevOps, DataOps, MLOps) and business unit executives to identify and develop pilot AI use cases.
Gather requirements from multiple stakeholders (business users, data scientists, security professionals, data analysts, operators) and develop the necessary processes and products in the technical implementation.
Define the AI architecture and recommend appropriate technologies from a pool of open-source and commercial offerings. Propose cloud, on-premises or hybrid deployment models, and ensure new tools are well-integrated with existing platforms and tools.
Audit AI tools and practices across data, models and software engineering with a focus on continuous improvement. Ensure a feedback mechanism to assess AI services, support model recalibration and retrain models.
Work closely with the client’s security and compliance leaders to foresee and mitigate risks, such as training data poisoning, AI model theft and adversarial samples, ensuring ethical AI implementation and restoring trust in the client’s AI Platform. Remain acquainted with upcoming regulations and best practices and recommend refinements to the client’s processes and technology adoption.
Skills And Qualifications:
Degree in engineering discipline, preferably Master of Engineering or Science, or equivalence.
5+ years' experience in the development of AI and machine learning projects
Proficiency in 2 or more AI domains, including business intelligence, customer data analytics, computer vision, and natural language processing.
Proficiency in most of the following machine learning libraries: Pandas, Scikit-Learn, TensorFlow, PyTorch, OpenAI, HuggingFace, OpenCV.
Experience in algorithm design and implementation
Experience in designing cloud or hybrid cloud solutions using AWS, Azure or GCP will be advantage.