1 hour 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
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