Support customer in adapting and deploying group-level ML solutions across business units on cloud platform.

Enhance R&D code into production-grade pipelines, setting up APIs, ensuring CI/CD compliance

Collaborate with infra teams to ensure operational readiness.

Bachelor’s degree in business, Information Technology or relevant disciplines

At least 4 years of working experience in Data Machine Learning

Hands-on experience deploying machine learning models in cloud environments, including containerization (e.g., Docker) and orchestration frameworks

Proficient in building and adapting ML pipelines using existing codebases or templates, with the ability to customize configurations for BU-specific needs

Solid understanding of CI/CD practices and MLOps workflows (e.g., GitHub, Cloud Build, model registries)

Experience designing and maintaining end-to-end ML pipelines, covering data ingestion, preprocessing, model serving, and logging

Skilled in API deployment using frameworks such as Flask or FastAPI; capable of containerizing and scaling model services

Strong proficiency in writing modular, testable, and production-grade Python code following software engineering best practices

Familiarity with relational and NoSQL databases; understanding of vector databases (e.g., FAISS, Pinecone, Chroma) is a strong advantage

Strong troubleshooting and debugging skills across data, code, infrastructure, and runtime layers

Experience with distributed data processing tools (e.g., Spark) and cloud-native services

Collaborative and results-driven, with the ability to work effectively across functions with Data Scientists, QA Engineers, and Product Managers

Good communication skills in both spoken and written English and Cantonese