We are seeking an innovative and experienced Machine Learning Engineer to join our AI + Data team, a cross-functional group spanning Operations, Technology, and Marketing. Our team's mission is to research, build, and deliver production-grade machine learning and Generative AI capabilities that help us better understand our customers, personalize experiences, and drive measurable business impact across insurance, banking, and wealth management globally.
As a Machine Learning Engineer, you will design, build, and operate the platforms, pipelines, and reusable patterns that take models from experimentation to production at scale. You will work with large-scale, diverse datasets including call center transcripts, insurance claims, digital transactions, and more, building systems that enhance the end-to-end customer experience.
This is a global role with exposure to markets in Canada, the U.S., and Asia, offering the opportunity to collaborate with cross-functional teams and deliver robust, scalable ML systems across multiple business segment
Position Responsibilities: - Reusable Patterns and Accelerators: Build reusable patterns for data, ML, and GenAI workloads, following MLOps, LLMOps, and AIOps best practices, and partner with delivery teams on implementation.
- CI/CD: Own the engineering backbone for ML delivery, including source control workflows, build and deployment pipelines, automated testing, spec-driven development, and release management.
- Infrastructure as Code: Provision and manage PaaS infrastructure using Terraform, with repeatable, version-controlled environments across development, staging, and production.
- Credential and Secrets Management: Implement secure credential handling using Azure Key Vault and managed identities, scoping access narrowly across services and pipelines.
- Scalable Infrastructure: Develop and maintain scalable ML platforms and serving infrastructure that support training, inference, monitoring, and lifecycle management of models in production.
- Data Pipeline Optimization: Partner with data engineers to build high-quality, well-tested feature and training pipelines that ensure efficient, reliable data processing for ML applications.
- Model Development and Deployment: Design, train, evaluate, and deploy machine learning models, and integrate large language models where they are the right tool, to solve complex business problems and improve operational efficiency.
- Model Performance and Reliability: Continuously monitor and improve models and systems for accuracy, latency, cost, drift, and reliability, with clear observability and alerting.
- Governance and Responsible AI: Ensure interoperability, data consistency, and responsible AI through strong API and data standards, metadata management, security-by-design, privacy controls, and model governance.
- Integration and Collaboration: Partner with data scientists, engineers, and business stakeholders to gather requirements and integrate ML solutions smoothly with existing systems.
- Innovation and Research: Stay current with emerging technologies and practices across data engineering, machine learning, and Generative AI, including RAG, vector search, model fine-tuning, and orchestration frameworks.
Required Qualifications: - Professional Experience: At least 4 years of experience in machine learning engineering, with a proven track record of building and deploying ML models and systems in production.
- Technical Proficiency: Strong programming skills in Python with hands-on experience in ML frameworks and libraries. Experience with Java or Scala for model serving and JVM-based pipelines is an asset, as is familiarity with GenAI tooling such as LangChain, LangGraph, or the OpenAI SDK.
- MLOps and CI/CD: Practical experience with model lifecycle tooling (for example MLflow, Azure Machine Learning, or Databricks) and with CI/CD pipelines using tools such as Jenkins, GitHub Actions, or Azure DevOps.
- Cloud and Infrastructure: Working knowledge of cloud platforms, containerization (Docker, Kubernetes), and infrastructure as code with Terraform.
- Educational Background: Bachelor's degree in Computer Science, Engineering, Statistics, or a related field. Equivalent technical experience is also considered.
Preferred Qualifications: - Machine Learning Expertise: Strong knowledge of machine learning algorithms, with experience adapting pre-trained and foundation models to domain-specific problems.
- Large-Scale Data Processing: Experience with distributed computing frameworks such as Spark, and with lakehouse architectures on Databricks or equivalent, including Delta and Unity Catalog for data and model governance.
- Data Engineering Skills: Solid understanding of data engineering principles, including data pipelines and ETL processes.
- Problem-Solving Ability: Exceptional problem-solving skills with the capacity to tackle complex technical challenges.
- Effective Communication: Excellent communication skills to effectively collaborate with cross-functional teams and convey technical concepts to non-technical stakeholders.
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.
#LI-Hybrid
The role being advertised is an existing vacancy.
Referenced Salary LocationToronto, Ontario
Working ArrangementHybrid
Salary range is expected to be between$94,430.00 CAD - $144,430.00 CAD
Employees also have the opportunity to participate in incentive programs and earn incentive compensation tied to business and individual performance. 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. If you are applying for this role outside of the primary location, please contact [redacted] for the salary range for your location.
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 [redacted] for more information about U.S.-specific paid time off provisions.
We use data and analytics technologies, such as artificial intelligence (AI), and automated processing tools, to analyze and process the information you provide to us or third parties in the application process. For more information, please refer to our personal information collection statement.
