Senior ML Engineer
Hong Kong
- Pay
- Pay not listed
- Work setting
Hybrid stated — work setting source
Working Arrangement Hybrid
Read the full posting- Employment
Full-time — employment source
Employment type in the employer’s listing: Full time
From the employer’s posting
What you’ll work on
Full postingBuild and maintain data pipelines and feature engineering workflows for Health AI models
Develop MLOps practices: experiment tracking, versioning, CI/CD, testing and monitoring
Build and operate model serving, APIs, batch inference and GenAI services
Implement RAG, agentic workflows, vector search and evaluation frameworks
Improve reliability, performance, cost and observability of Health AI platforms
From the employer’s posting
Design, build, and maintain scalable data pipelines and feature engineering workflows
Develop robust MLOps practices across the model lifecycle
Build and operate reliable model serving capabilities, APIs, batch inference jobs, and GenAI services
Implement modern AI engineering patterns such as retrieval augmented generation, prompt orchestration, function calling, agentic workflows, vector search, and evaluation frameworks
Improve reliability, performance, cost efficiency, and observability of Health AI platforms
What you’ll bring
All qualificationsCore experience
- Proven experience 6 years+ designing and operating scalable data pipelines, feature engineering workflows, and model training pipelines for production AI/ML systems.
- 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.
- 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.
Qualification wording
Proven experience 6 years+ designing and operating scalable data pipelines, feature engineering workflows, and model training pipelines for production AI/ML systems.
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.
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.
Tools in this posting
- Python
- SQL
- Azure
- Databricks
- Spark
Source — Tool mentions in context
- 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, 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 modern GenAI engineering patterns, including retrieval augmented generation, vector search, prompt orchestration, function calling, agentic workflows, and evaluation frameworks.
- 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, with practical experience in ML frameworks, data processing tools, orchestration pipelines, and GenAI engineering packages such as LangChain, LangGraph, ADK, or CrewAI.
About Manulife Financial
We strive to attract, develop and retain a workforce that is as diverse as the customers we serve and to foster an inclusive work environment that embraces the strength of cultures and individuals.
In the employer’s words · Read in context
Posting record
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- First seen by us
- Aug 19, 2026 · 65 days ago
- Recorded sightings
- 181
- Last seen by us
- Oct 23, 2026
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Job description
We are seeking a highly skilled Senior Machine Learning Engineer to join our Health AI Squad at Manulife Asia, based in Singapore / Hong Kong. This role will focus on building scalable, production-grade AI/ML and GenAI capabilities that advance Health AI across Asia. As part of the regional Health AI team, you will design, build, and operate robust data pipelines, model services, and GenAI platforms that turn complex health data into practical solutions, while applying modern machine learning and Generative AI patterns such as retrieval augmented generation, prompt engineering, function calling, and agentic workflows to help deliver Health AI at scale across multiple Asian markets.
Position Responsibilities:
- Design, build, and maintain scalable data pipelines and feature engineering workflows to support Health AI model development, evaluation, and production deployment.
- Develop robust MLOps practices across the model lifecycle, including experiment tracking, model versioning, CI/CD, automated testing, monitoring, and governance-ready release processes.
- Build and operate reliable model serving capabilities, APIs, batch inference jobs, and GenAI services that can scale across multiple Health AI use cases and markets.
- Partner with data scientists, data engineers, AI developers, and IT teams to productionize machine learning and GenAI solutions using approved cloud architecture, security standards, and reusable engineering patterns.
- Implement modern AI engineering patterns such as retrieval augmented generation, prompt orchestration, function calling, agentic workflows, vector search, and evaluation frameworks for Health AI solutions.
- Improve reliability, performance, cost efficiency, and observability of Health AI platforms through logging, monitoring, alerting, model performance tracking, and continuous optimization.
- Create reusable templates, tools, and engineering standards that help teams accelerate the delivery of production-grade Health AI capabilities.
- Stay current with advances in machine learning engineering, MLOps, cloud-native AI platforms, and Generative AI architecture to continuously strengthen Health AI delivery.
Required Qualifications:
- An advanced degree in Computer Science, Machine Learning, Engineering, Data Science, Statistics, or another quantitative field, complemented by 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, 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 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.
What motivates you?
- You obsess about customers, listen, engage and act for their benefit.
- You think big, with curiosity to discover ways to use your agile approach and enable business outcomes.
- You thrive in teams and enjoy getting things done together.
- You take ownership and build solutions, focusing on what matters.
- You do what is right, work with integrity and speak up.
- You share your humanity, helping us build a diverse and inclusive work environment for everyone.
About Manulife and John Hancock
Manulife Financial Corporation is a leading international financial services provider, helping people make their decisions easier and lives better. To learn more about us, visit https://www.manulife.com/en/about/our-story.html.
Manulife is an Equal Opportunity Employer
At Manulife/John Hancock, we embrace our diversity. We strive to attract, develop and retain a workforce that is as diverse as the customers we serve and to foster an inclusive work environment that embraces the strength of cultures and individuals. We are committed to fair recruitment, retention, advancement and compensation, and we administer all of our practices and programs without discrimination on the basis of race, ancestry, place of origin, colour, ethnic origin, citizenship, religion or religious beliefs, creed, sex (including pregnancy and pregnancy-related conditions), sexual orientation, genetic characteristics, veteran status, gender identity, gender expression, age, marital status, family status, disability, or any other ground protected by applicable law.
It is our priority to remove barriers to provide equal access to employment. A Human Resources representative will work with applicants who request a reasonable accommodation during the application process. All information shared during the accommodation request process will be stored and used in a manner that is consistent with applicable laws and Manulife/John Hancock policies. To request a reasonable accommodation in the application process, contact hr@manulife.com.
Working Arrangement
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Hong Kong
Working Arrangement Hybrid
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