11 days ago
Toronto, CanadaMid Level
Responsibilities
- Build reusable patterns and accelerators for data, machine learning, and Generative AI workloads using MLOps, LLMOps, and AIOps practices.
- Own source control workflows, build and deployment pipelines, automated testing, 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 model lifecycle management.
- Build reliable feature and training data pipelines with data engineering partners.
- Design, train, evaluate, deploy, monitor, and improve machine learning models and integrate large language models where appropriate.
- Establish observability, alerting, API and data standards, metadata management, privacy controls, security-by-design, and model governance.
- Collaborate with data scientists, engineers, delivery teams, and business stakeholders to integrate ML solutions.
- Research emerging practices including retrieval-augmented generation, vector search, model fine-tuning, and orchestration frameworks.
Requirements
- At least 4 years of professional experience in machine learning engineering, including building and deploying ML models and systems in production.
- Strong programming skills in Python and hands-on experience with machine learning frameworks and libraries.
- Experience with model lifecycle tooling such as MLflow, Azure Machine Learning, or Databricks.
- Experience building CI/CD pipelines with 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.
- Java or Scala experience for model serving and JVM-based pipelines is an asset.
- Familiarity with LangChain, LangGraph, or the OpenAI SDK is an asset.
- Strong knowledge of machine learning algorithms and experience adapting pre-trained and foundation models to domain-specific problems.
- Experience with distributed computing frameworks such as Spark and lakehouse architectures on Databricks or equivalent, including Delta and Unity Catalog.
- 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 supporting learning, career growth, well-being, and inclusion.
- Eligible employees may receive health, dental, mental health, vision, disability, life, AD&D, adoption/surrogacy, wellness, and employee/family assistance benefits.
- Retirement savings plans, including pension and a global share ownership plan with employer matching contributions, plus financial education and counseling resources.
- Paid holidays, vacation, personal, and sick days, and statutory leaves of absence in Canada.
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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.