AI Data Engineer (Hong Kong/Macau/Zhuhai Local)

AI Data Engineer (Hong Kong/Macau/Zhuhai local)

One. Job Responsibilities

1. Responsible for collecting, cleaning, and annotating AI project datasets to ensure data quality and availability.

2. Participate in the training process of large language models, including data preprocessing, model tuning and validation.

3. Proficient in using data processing tools to efficiently complete data conversion, storage and management tasks.

4. Assist in the development and verification of AI applications, providing data support for model optimization.

II. Professional academic requirements

1. Educational background: Bachelor's degree or above, Computer Science, Software Engineering, Mathematics, Statistics, Data Science or related fields are preferred.

Master/Doctorate preferred: For positions with in-depth research needs, a master's or doctoral degree is better.

2. Professional courses: Have solid mathematical foundations (such as linear algebra, probability theory, calculus) and computer fundamentals (such as data structures, algorithm design).

Three. Tools and technical requirements

2. Deep learning frameworks: Familiar with TensorFlow, PyTorch, Keras, etc., mainstream deep learning frameworks.

3. Data processing tools: Proficient in using Pandas, NumPy, etc. data processing libraries, as well as SQL, NoSQL databases.

4. Big data tools: Familiar with Spark, Hadoop, etc., big data processing tools.

5. Version control and deployment: Master Git and other version control tools, understand Docker, Kubernetes, etc. deployment tools.

Four. Soft skills requirements

1. Communication and collaboration: Have good communication skills, be able to work efficiently with team members, and write clear technical documentation.

2. Problem-solving ability: Have the ability to quickly learn and solve problems, and be able to find solutions in complex projects.

3. Innovation and research ability: Maintain curiosity about new technologies, be able to track cutting-edge technologies and apply them to actual projects.

4. Industry understanding: Able to combine AI technology with actual business scenarios, understand industry pain points and design solutions.

5. Ethics and compliance: Focus on data privacy and model fairness, ensuring that technology applications meet ethical and regulatory requirements.

1. Project experience: Have 1 to 3 years of AI project data processing and development experience, those who have participated in large-scale projects or open source projects are preferred, if not, those with excellent innovation and basic skills are also eligible, and those who have in-depth understanding of large model evaluation and competitive product performance.

2. Domain knowledge: Familiar with natural language processing, computer vision, recommendation systems, etc. AI application fields.

3. Model optimization: Have practical experience in model training, tuning, and deployment.

Six. Other requirements

2. Teamwork: Able to play a key role in the team, with strong sense of responsibility and stress resistance.