Artificial Intelligence Specialist

Key Responsibilities:

1. AI Solution Design: Collaborate with stakeholders to understand business requirements and design AI solutions that meet these needs.

2. Model Development: Develop, train, and optimize machine learning models using frameworks such as TensorFlow, PyTorch, or Scikit-learn.

4. Data Engineering: Work with large datasets to clean, pre process, and structure data for training and deployment of AI models. Proficiency with SQL and NoSQL databases is required.

5. Software Development: Write efficient, maintainable, and scalable code in languages such as Python, Java, or C++. Implement AI algorithms and integrate them into software applications.

6. System Integration: Integrate AI solutions with existing systems and workflows. Use APIs and other integration methods to ensure seamless functionality.

7. Performance Optimization: Optimize AI models for performance, accuracy, and scalability. Use techniques such as hyperparameter tuning, model pruning, and quantization.

8. Deployment: Deploy AI models in production environments using tools such as Docker, Kubernetes, and cloud platforms (AWS, Azure, GCP).

9. Monitoring and Maintenance: Monitor the performance of AI systems, troubleshoot issues, and implement improvements. Ensure models remain accurate and relevant over time.

10. Research and Development: Stay current with the latest AI research and technologies. Experiment with new methodologies and tools to improve existing solutions and develop new capabilities.

11. Collaboration: Work closely with cross-functional teams, including data scientists, software engineers, product managers, and business analysts, to deliver high-quality AI solutions.

Required Qualifications:

Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

Experience: 3+ years of experience in AI/ML and software engineering roles.

Proficient in Mandarin language

Technical Skills:

- Proficiency in programming languages such as Python, Java, and C++.

- Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).

- Strong understanding of AI/ML algorithms and techniques, including supervised and unsupervised learning, reinforcement learning, and deep learning.

- Familiarity with data processing and analysis tools (e.g., Pandas, NumPy).

- Knowledge of cloud platforms and services (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).

- Experience with version control systems (e.g., Git) and CI/CD pipelines.