23 hours ago
Toronto, CanadaMid Level / Senior
Responsibilities
- Build reusable patterns and accelerators for data, machine learning, and Generative AI workloads.
- Own CI/CD workflows, build and deployment pipelines, automated testing, source control, and release management for ML delivery.
- Provision and manage repeatable development, staging, and production infrastructure with Terraform.
- Implement secure credential and secrets management using Azure Key Vault and managed identities.
- Develop scalable ML platforms and model-serving infrastructure for training, inference, monitoring, and lifecycle management.
- Build and optimize feature and training data pipelines with data engineering partners.
- Design, train, evaluate, and deploy machine learning models and integrate large language models where appropriate.
- Monitor and improve model and system accuracy, latency, cost, drift, observability, and reliability.
- Implement API and data standards, metadata management, privacy controls, security-by-design, and model governance.
- Collaborate with data scientists, engineers, and business stakeholders to integrate ML solutions into existing systems.
- Stay current with RAG, vector search, model fine-tuning, and orchestration frameworks.
Requirements
- At least 4 years of experience in machine learning engineering, including building and deploying ML models and systems in production.
- Strong Python programming skills and hands-on experience with machine learning frameworks and libraries.
- Experience with model lifecycle tooling such as MLflow, Azure Machine Learning, or Databricks.
- Experience with CI/CD pipelines and tools such as Jenkins, GitHub Actions, or Azure DevOps.
- Working knowledge of cloud platforms, Docker, Kubernetes, and Terraform.
- Bachelor's degree in Computer Science, Engineering, Statistics, or a related field, or equivalent technical experience.
- Experience with Java or Scala for model serving and JVM-based pipelines is an asset.
- Familiarity with LangChain, LangGraph, or the OpenAI SDK is an asset.
- Knowledge of machine learning algorithms and adapting pre-trained or foundation models to domain-specific problems.
- Experience with Spark and lakehouse architectures on Databricks or equivalent, including Delta and Unity Catalog, is preferred.
- Understanding of data engineering principles, data pipelines, and ETL processes.
- Strong problem-solving, communication, and cross-functional collaboration skills.
Benefits
- Hybrid working arrangement in Toronto, Ontario.
- Flexible environment with learning, career growth, well-being, and inclusion support.
- Eligible employees may receive health, dental, mental health, vision, disability, life, adoption/surrogacy, wellness, and employee/family assistance benefits.
- Retirement savings plans, including pension and global share ownership with employer matching, plus financial education and counseling resources.
- Paid holidays, vacation, personal and sick days, and statutory leaves of absence in Canada.
- Equal opportunity employment and reasonable accommodation support.
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About Manulife
Manulife is a Toronto‑headquartered public financial services company that provides life and health insurance, retirement plans, and wealth and asset management to individuals and institutions. It earns premiums and fee income from insurance, investment, and advisory products delivered across Canada, Asia, and Europe, and operates as John Hancock in the United States. Founded in 1887, the company is listed on the Toronto Stock Exchange.