Senior ML Engineer
HybridHong Kong, Hong Kong
Job Summary
Design, build, and maintain scalable data pipelines, feature engineering workflows, and model serving capabilities including APIs, batch inference jobs, and GenAI services. Develop robust MLOps practices across the full model lifecycle, covering experiment tracking, versioning, CI/CD, automated testing, monitoring, and governance-ready release processes. Implement modern AI engineering patterns such as retrieval augmented generation, prompt orchestration, function calling, agentic workflows, and vector search to productionize Health AI solutions. Partner with data scientists, data engineers, and IT teams to optimize platform reliability, performance, cost efficiency, and observability through logging, alerting, and continuous optimization. Create reusable templates and engineering standards to accelerate delivery of production-grade capabilities across multiple Asian markets.
Required Qualifications
- An advanced degree in Computer Science, Machine Learning, Engineering, Data Science, Statistics, or another quantitative field
- strong hands-on experience in production machine learning engineering
- Proven experience 6 years+ designing and operating scalable data pipelines, feature engineering workflows, and model training pipelines for production AI/ML systems
- Strong MLOps expertise across the full model lifecycle, including experiment tracking, model versioning, CI/CD, automated testing, deployment automation, monitoring, and release governance
- Hands-on experience building and scaling model serving capabilities, APIs, batch inference pipelines, and GenAI services on cloud platforms such as Azure, Databricks, Spark, and containerized deployment environments
- Advanced programming skills in Python and SQL
- practical experience in ML frameworks, data processing tools, orchestration pipelines, and GenAI engineering packages such as LangChain, LangGraph, ADK, or CrewAI
- Practical knowledge of modern GenAI engineering patterns, including retrieval augmented generation, vector search, prompt orchestration, function calling, agentic workflows, and evaluation frameworks
- Experience improving platform reliability, performance, cost efficiency, and observability through logging, monitoring, alerting, model performance tracking, and continuous optimization
- Ability to translate Health AI use cases into secure, reusable, production-grade engineering patterns in partnership with data science, data engineering, IT, and business teams
- Strong communication, ownership, and mentoring skills, with the ability to guide engineering best practices and collaborate effectively across technical and non-technical stakeholders
- Must be able to lift 50 lbs
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