About This Role
This engineering role focuses on designing, developing, and deploying machine learning solutions to support business objectives. The successful candidate will collaborate with data scientists, data engineers, and business stakeholders to build scalable AI/ML models and integrate them into production environments.
Responsibilities
- Develop, train, and optimize machine learning and deep learning models
- Design and implement end-to-end ML pipelines for data preparation, model training, deployment, and monitoring
- Collaborate with data engineers to build scalable data processing solutions
- Deploy and maintain ML models in cloud and on-premises environments
- Monitor model performance and implement continuous improvement processes
- Conduct model evaluation, testing, and troubleshooting
- Collaborate with business and technical teams to translate requirements into AI/ML solutions
- Document technical designs, methodologies, and implementation details
Requirements
- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, or a related discipline
- 3 years of experience in machine learning model development and deployment
- Strong programming skills in Python
- Experience with ML frameworks such as Scikit-learn, TensorFlow, PyTorch, or XGBoost
- Experience building and maintaining ML pipelines and MLOps practices
- Familiarity with cloud platforms such as Azure, AWS, or GCP
- Strong understanding of data structures, algorithms, statistics, and model evaluation techniques
- Experience working with SQL and large datasets
- Business-level proficiency in Mandarin, Cantonese, and English is mandatory
Nice to have:
- Experience with Generative AI and Large Language Models (LLMs)
- Knowledge of Docker, Kubernetes, and CI/CD practices
- Experience with vector databases, RAG architectures, and AI application development
- Exposure to financial services or regulated industries
