Our client is seeking a high-caliber Machine Learning Engineer to bridge the gap between model development and large-scale production. As part of the Global Agentic AI Program, you will be responsible for the "industrialization" of AI. Your mission is to build the robust, scalable pipelines that allow our LLM and ML models to run reliably across thousands of stores and digital touchpoints worldwide.
Key Responsibilities:
• Dockerize and deploy ML/LLM models across the company’s AI ecosystem, ensuring they are optimized for performance and resource efficiency.
• Build and maintain end-to-end MLOps workflows, including Continuous Integration, Continuous Deployment, and Continuous Training to prevent model decay.
• Manage container orchestration (e.g., Kubernetes) and cloud-native patterns to support high-concurrency applications.
• Implement robust monitoring for model drift, data quality, and system health, ensuring rapid incident response and recovery.
• Align AI application logic with downstream retail systems (POS, Inventory, CRM) and front-end Generative UI components.
Requirements:
• Bachelor's or Master's degree in Computer Science, Mathematics, Engineering, or a related field.
• 3+ years of professional experience in Artificial Intelligence, Machine Learning Engineering, DevOps, or Model Deployment.
• Hands-on experience with Distributed Systems and production-grade performance tuning.
• Proven knowledge of LangGraph or similar frameworks, with a deep understanding of how to test and scale agentic loops.
• Expert in Docker and Kubernetes, with experience in cloud-native deployment patterns for orchestrating large-scale AI services.
• High coding proficiency in Python with a focus on service design, concurrency, and failure handling.
• Professional English fluency. Cantonese or Mandarin is a significant plus.
