Machine Learning Engineer

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 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 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.
• MLOps and CI/CD:Practical experience with model lifecycle tooling (for example MLflow, Azure Machine Learning, or Databricks) and with CI/CD pipelinesusing 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 framework