Manulife's bold ambition is to become a digital, customer-first leader. To achieve this, we've made significant investments in Advanced Analytics and AI capabilities. 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 chance 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 andservinginfrastructure that support training, inference, monitoring, and lifecycle management of models in production. Data Pipeline Optimization: Partner with data engineers to build high-quality, well-testedfeatureand 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.

📌 Machine Learning Engineer (Winnipeg)
🏢 Manulife
📍 Winnipeg