2 months ago
Hong Kong, Hong KongSenior
Responsibilities
- Design, build, and maintain scalable data pipelines and feature engineering workflows for Health AI model development, evaluation, and production deployment.
- Develop MLOps practices covering experiment tracking, model versioning, CI/CD, automated testing, monitoring, and governance-ready releases.
- Build and operate model serving capabilities, APIs, batch inference jobs, and scalable GenAI services.
- Implement retrieval augmented generation, prompt orchestration, function calling, agentic workflows, vector search, and evaluation frameworks.
- Improve platform reliability, performance, cost efficiency, and observability through logging, monitoring, alerting, model performance tracking, and continuous optimization.
- Create reusable templates, tools, and engineering standards for production-grade Health AI delivery.
- Collaborate with data science, data engineering, AI development, IT, and business teams to productionize secure AI/ML solutions.
- Stay current with advances in machine learning engineering, MLOps, cloud-native AI platforms, and Generative AI architecture.
Requirements
- Advanced degree in Computer Science, Machine Learning, Engineering, Data Science, Statistics, or another quantitative field, plus strong hands-on production machine learning engineering experience.
- 6+ years of experience designing and operating scalable data pipelines, feature engineering workflows, and model training pipelines for production AI/ML systems.
- Strong MLOps expertise across 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.
- Experience with Azure, Databricks, Spark, and containerized deployment environments.
- Advanced programming skills in Python and SQL, with practical experience in ML frameworks, data processing tools, orchestration pipelines, and GenAI engineering packages such as LangChain, LangGraph, ADK, or CrewAI.
- Practical knowledge of retrieval augmented generation, vector search, prompt orchestration, function calling, agentic workflows, and evaluation frameworks.
- Experience improving 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.
- Strong communication, ownership, mentoring, collaboration, and stakeholder-management skills.
Benefits
- Hybrid working arrangement.
- Opportunity to build Health AI capabilities across multiple Asian markets.
- Inclusive and diverse work environment with equal opportunity and reasonable accommodation support.
Tech Stack
Categories
About Manulife
Manulife is a Toronto‑headquartered public financial services company that provides life and health insurance, retirement plans, and wealth and asset management to individuals and institutions. It earns premiums and fee income from insurance, investment, and advisory products delivered across Canada, Asia, and Europe, and operates as John Hancock in the United States. Founded in 1887, the company is listed on the Toronto Stock Exchange.