Role:

We are seeking a highly skilled and proactive Machine Learning Engineer to join our team. The ideal candidate will have a strong foundation in machine learning, with hands-on experience in traditional ML, Generative AI, or agentic AI. A basic understanding of supply chain business and expertise in working with large, complex supply chain datasets are essential for success in this role.

The Machine Learning Engineer will be instrumental in developing cutting-edge data-driven models and algorithms. Key responsibilities include creating innovative use cases, developing proof-of-concept solutions, and leading the deployment of Supply Chain AI applications. And work closely with stakeholders to gather business requirements, manage data processing, build interactive business intelligence dashboards, and provide valuable insights to support our digital transformation target.

Responsibilities:

·End-to-End AI Solution Ownership: Design, develop, and deploy scalable AI/ML solutions for Sales, Manufacturing, and Logistics domains using Scikit-learn, TensorFlow, or PyTorch. Ensure seamless integration of models into production systems (APIs, cloud services, or edge devices).

·Stakeholder-Driven Problem Framing: Partner with business units to translate operational challenges into data/AI requirements. Define KPIs and success metrics for cross-domain initiatives.

·Full-Cycle Data & Model Development: Implement robust pipelines for data cleaning, transformation, and feature engineering. Build and optimize models (predictive maintenance, demand forecasting, route optimization). Create interactive Power BI dashboards to communicate insights.

·Model Governance: Establish Model Governance frameworks for performance monitoring, bias detection, and interpretability while implementing MLflow-powered CI/CD pipelines for automated model retraining and lifecycle management.

·Cross-Functional Collaboration: Work with data engineers, BI engineers, and application developers to establish end-to-end analytical pipelines and machine learning operations.

·Effective Communication: Clearly convey complex AI methodologies and results to both technical and non-technical audiences.

·Digital Transformation Partnership: Collaborate closely with the data team to drive digital transformation initiatives.

Requirements:

·Education: Bachelor's or advanced degree in Mathematics, Statistics, Computer Science, or a related field.

·Experience: Preferably 3+ years of hands-on experience working on ML/AI projects.

·Model Frameworks: Proficiency in popular machine learning frameworks like Scikit-learn, PyTorch, and TensorFlow, with proven experience in environment setup and management.

·Technical Expertise:

o Strong understanding of statistical machine learning and deep learning techniques, especially for text, graph, and time-series data (e.g., OCR, NLP, sentiment analysis, forecasting).

o Experience with large language models (LLMs) is highly desirable.

o Experience in model management with familiarity in MLflow for tracking, versioning, and deploying machine learning models.

·Containerization & Orchestration: Knowledge of Docker and Kubernetes for application deployment and scalability.

·Solution Optimization: Proven ability to enhance existing ML solutions through pre- and post-processing techniques, fine-tuning, performance evaluation, visualization, and testing.

· Version Control & CI/CD: Experience with CI/CD pipelines and version control systems like Git.

·Data Visualization: Proficiency in Power BI for creating actionable visualizations.

·Cloud Platforms: Familiarity with major cloud platforms (e.g., Azure DevOps, Databricks, or Data Factory) is a plus.

·Real-Time Data Technologies: Exposure to real-time data technologies such as Apache Kafka, Azure Event Hubs, and KQL is a plus.

Character:

·Strong sense of responsibility and ability to work collaboratively as a team player.

·A passion for coding, programming, innovation, and problem-solving.

·Self-driven with commitment to continuous AI/ML learning.

·Good communication skills including spoken, written English and Chinese.