Job Description

Responsibilities:

• 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.

Requirements:

• 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 skill in both spoken and written English and Chinese.

If you're interested in this role, please click the Apply button above or via Linkers website at https://jobs.linkerscareers.com/ with your CV.

If you would like to have a further discussion about this role, please contact us at +852 2391 2813.

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