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Manulife

Machine Learning Engineer

Job Posted 8 Days Ago Posted 8 Days Ago
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In-Office
2 Locations
Mid level
In-Office
2 Locations
Mid level
The Machine Learning Engineer will develop and deploy generative AI applications, implement MLOps workflows, and ensure data privacy and compliance in financial contexts.
The summary above was generated by AI

Join Manulife and be part of an exceptionally dedicated team where your work ethic will determine the success of our innovative solutions. Apply now to compete at the forefront of generative AI and financial technology!

At Manulife, we offer a uniquely collaborative environment where you will craft, develop, and deploy generative AI applications. These include copilots, summarization tools, conversational agents, and retrieval-augmented generation (RAG) pipelines to support both internal teams and client-facing platforms.

As a Machine Learning Engineer, your role will be crucial in implementing and fine-tuning large language models, integrating them with vector databases, and building scalable RAG systems. Collaborating with financial domain experts, you will understand complex use cases in wealth management, retirement planning, risk analysis, and client servicing. You'll also build and maintain APIs, model serving layers, and secure pipelines to ensure robust deployment and monitoring.

In this ambitious role, you will implement MLOps workflows to manage the full model lifecycle, including versioning, evaluation, and drift detection. Ensuring data privacy, security, compliance (e.g., FINRA, SEC), and responsible AI practices in all model deployments, you will contribute to our world-class standards!

Required Qualifications
  • Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
  • 3+ years of experience in machine learning engineering, including experience with production-level deployment.
  • Proficient in Python and experienced with generative AI and machine learning frameworks (e.g., Hugging Face Transformers, LangChain, PyTorch).
  • Experience with cloud platforms (AWS, Azure, or GCP), containerization (Docker, Kubernetes), and CI/CD workflows.
  • Experience building generative AI applications in a financial context (e.g., advisor tools, regulatory automation, portfolio analysis).
  • Deep understanding of LLM fine-tuning, embeddings, and timely optimization.
  • Knowledge of MLOps tools (MLflow, SageMaker, Azure ML) and model monitoring frameworks.
Preferred Qualifications
  • Familiarity with enterprise architecture, APIs, and secure software design in regulated environments.
  • Familiarity with timely engineering.
  • Awareness of financial regulations and compliance considerations (e.g., data retention, audibility, explainable).
When you join our team:
  • We’ll empower you to learn and grow the career you want.
  • We’ll recognize and support you in a flexible environment where well-being and inclusion are more than just words.
  • As part of our global team, we’ll support you in shaping the future you want to see.

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 recruitment@manulife.com.

Referenced Salary Location

Toronto, Ontario

Working Arrangement

Hybrid

Salary range is expected to be between

$75,880.00 CAD - $140,920.00 CAD

If you are applying for this role outside of the primary location, please contact recruitment@manulife.com for the salary range for your location. The actual salary will vary depending on local market conditions, geography and relevant job-related factors such as knowledge, skills, qualifications, experience, and education/training. Employees also have the opportunity to participate in incentive programs and earn incentive compensation tied to business and individual performance.

Manulife offers eligible employees a wide array of customizable benefits, including health, dental, mental health, vision, short- and long-term disability, life and AD&D insurance coverage, adoption/surrogacy and wellness benefits, and employee/family assistance plans. We also offer eligible employees various retirement savings plans (including pension and a global share ownership plan with employer matching contributions) and financial education and counseling resources. Our generous paid time off program in Canada includes holidays, vacation, personal, and sick days, and we offer the full range of statutory leaves of absence. If you are applying for this role in the U.S., please contact recruitment@manulife.com for more information about U.S.-specific paid time off provisions.

Top Skills

AWS
Azure
Azure Ml
Docker
GCP
Hugging Face Transformers
Kubernetes
Langchain
Mlflow
Python
PyTorch
Sagemaker

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