Machine Learning Engineer

About This Role

Manulife is seeking an innovative and experienced Machine Learning Engineer to join its AI + Data team, a cross-functional group spanning Operations, Technology, and Marketing. This global role offers exposure to markets in Canada, the U.S., and Asia, with opportunities to collaborate with cross-functional teams and deliver robust, scalable ML systems across multiple business segments. You will design, build, and operate the platforms, pipelines, and reusable patterns that take models from experimentation to production at scale.

Responsibilities

• Build reusable patterns for data, ML, and GenAI workloads, following MLOps, LLMOps, and AIOps best practices, and partner with delivery teams on implementation
• Own the engineering backbone for ML delivery, including source control workflows, build and deployment pipelines, automated testing, spec-driven development, and release management
• Provision and manage PaaS infrastructure using Terraform, with repeatable, version-controlled environments across development, staging, and production
• Implement secure credential handling using Azure Key Vault and managed identities, scoping access narrowly across services and pipelines
• Develop and maintain scalable ML platforms and serving infrastructure that support training, inference, monitoring, and lifecycle management of models in production
• Partner with data engineers to build high-quality, well-tested feature and training pipelines that ensure efficient, reliable data processing for ML applications
• 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
• Continuously monitor and improve models and systems for accuracy, latency, cost, drift, and reliability, with clear observability and alerting
• Ensure interoperability, data consistency, and responsible AI through strong API and data standards, metadata management, security-by-design, privacy controls, and model governance
• Partner with data scientists, engineers, and business stakeholders to gather requirements and integrate ML solutions smoothly with existing systems
• 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

Requirements

• 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
• Strong programming skills in Python with hands-on experience in ML frameworks and libraries
• 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