Key Responsibilities
- Develop and maintain production-grade Python applications with clean architecture and modular code structure.
- Build and deploy RESTful APIs for ML models using frameworks such as FastAPI or Flask.
- Ensure code quality through unit testing, integration testing, and performance validation.
- Optimise systems for readability, maintainability, and runtime performance.
- Implement CI/CD pipelines using GitHub Actions, Jenkins, or Airflow to automate deployment workflows.
- Containerise ML services using Docker and deploy with high-performance tools like Gunicorn.
- Work with structured/tabular data using NumPy, Pandas, and integrate with PostgreSQL, MySQL, or data lake platforms like Spark or Databricks.
- Collaborate with cross-functional teams to support ML projects in domains such as space, logistics, energy, and insurance.
- Contribute to infrastructure monitoring and auto-scaling strategies in Kubernetes environments.
Qualifications
- 4+ years of experience in Python development with a focus on backend and data engineering.
- 2+ years of experience deploying ML models via APIs in production environments.
- Strong understanding of software engineering principles and production-grade code packaging.
- Experience with performance testing, stress testing, and system optimisation.
- Familiarity with orchestration tools such as Kubeflow, Ansible, or Apache Airflow.
- Working knowledge of CI/CD practices and container orchestration.
- Basic understanding of ML principles, model lifecycle, and evaluation techniques like cross-validation.
- Experience implementing monitoring systems using tools like Grafana, ELK Stack, MLflow, or ClearML.
- Exposure to infrastructure components such as databases, file storage, caching systems, and message queues.
If you're interested in this role, please send your latest resume to cheryl.NG@hays.com.hk or contact Cheryl Ng at +852 2101 0081.