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Manulife

Senior Platform Engineer

Toronto, ON, Canada
Waterloo, ON, Canada

Pay: $113,000.00 CAD - $163,000.00 CAD
Job type: Full-time
Apply by: Oct 16, 2026
Posting language: English
Posted: Sep 28, 2026

Apply About employer

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Original job description

As a Senior Platform Engineer, you will build and operate the shared platform that application teams use to deploy and run services on Azure Kubernetes Service. You will work with infrastructure defined through Terraform and delivered using GitHub Actions and GitOps practices.

This position provides broad technical ownership across continuous and progressive delivery, multi-region resiliency, disaster recovery, policy and compliance automation, and AI-assisted platform tooling. You will own platform capabilities from design and development through implementation and measurement, while establishing standards and reusable solutions that support engineering teams across the organization.

Position Responsibilities:

Build and operate continuous and progressive delivery capabilities for services running on Azure Kubernetes Service.
This includes GitHub Actions pipelines, Terraform modules, GitOps reconciliation, and canary and blue/green deployments through a service mesh, supported by versioning, phased rollouts, and tested rollback procedures.
Own and continuously improve shared CI/CD workflows and reusable Terraform module catalogues that application teams can confidently adopt.
Transform recurring platform requests into self-service capabilities and automated guardrails, while creating reference implementations, standards, and documentation for other engineers.
Design and test highly available, resilient solutions across multiple Azure regions.
Automate regional failover across infrastructure, traffic-routing components, and dependent Azure services, and conduct controlled chaos experiments to validate recovery capabilities.
Integrate observability, service-level indicators, service-level objectives, release gating, and rollback capabilities into the delivery process so that release outcomes are evaluated using service behavior.
Establish secure and compliant delivery paths by default through Azure Policy, in-cluster admission controls, and workload identity.
Design and deliver AI-assisted platform tooling using agent-based solutions and the Model Context Protocol for onboarding, configuration validation, access auditing, drift detection, and incident triage.
Incorporate appropriate approval gates, checkpointing, and auditability into AI-assisted platform solutions.

Required Qualifications:

8+ years of experience in software, platform, or infrastructure engineering, including ownership of at least one end-to-end platform capability used by other teams.
8+ years of experience working with Azure in an enterprise environment, including Entra ID, workload identity, networking, governance, Azure Policy, Key Vault, and managed data and messaging services.
5+ years of experience operating production Kubernetes environments across multiple clusters and regions, including experience with a service mesh such as Istio or an equivalent technology.
5+ years of experience supporting progressive delivery through canary or blue/green deployment approaches.
4+ years of experience with Terraform and CI/CD, including developing reusable modules used by other teams.
4+ years of experience developing GitHub Actions reusable workflows that incorporate approval gating and OpenID Connect federation with Azure.
2+ years of production experience with GitOps technologies such as Flux CD or Argo CD.
Production experience working with Python, Bash, YAML, and containers.
2+ years of hands-on experience with observability and policy as code, including an application performance monitoring platform such as New Relic or Datadog, SLI and SLO development, and admission-control policies enforced within Kubernetes clusters.
Current, hands-on experience building solutions using large language models, including agent frameworks, tool calling, or the Model Context Protocol, and applying these technologies to infrastructure or operational challenges.
Experience delivering technology solutions in a regulated enterprise environment with formal change-management, control, and audit requirements.

Preferred Qualifications:

Experience with chaos engineering and formal disaster-recovery testing using Chaos Mesh, Azure Chaos Studio, or an equivalent technology.
Experience building Model Context Protocol servers or agentic workflows that incorporate state management, checkpointing, and human approval gates using Azure OpenAI or Azure AI Foundry.
Experience operating a service mesh at scale, including traffic management, distributed tracing, and mesh observability.
Experience with API management platforms and the automated promotion of API configurations between environments.

When you join our team:

We’ll empower you to learn and grow the career you want.
We’ll recognize and support you in a flexible environment where well-being and inclusion are more than just words.
As part of our global team, we’ll support you in shaping the future you want to see.

The role being advertised is an existing vacancy.

About Manulife and John Hancock

Manulife Financial Corporation is a leading international financial services provider, helping people make their decisions easier and lives better. To learn more about us, visit https://www.manulife.com/en/about/our-story.html.

Manulife is an Equal Opportunity Employer

At Manulife/John Hancock, we embrace our diversity. We strive to attract, develop and retain a workforce that is as diverse as the customers we serve and to foster an inclusive work environment that embraces the strength of cultures and individuals. We are committed to fair recruitment, retention, advancement and compensation, and we administer all of our practices and programs without discrimination on the basis of race, ancestry, place of origin, colour, ethnic origin, citizenship, religion or religious beliefs, creed, sex (including pregnancy and pregnancy-related conditions), sexual orientation, genetic characteristics, veteran status, gender identity, gender expression, age, marital status, family status, disability, or any other ground protected by applicable law.

It is our priority to remove barriers to provide equal access to employment. A Human Resources representative will work with applicants who request a reasonable accommodation during the application process. All information shared during the accommodation request process will be stored and used in a manner that is consistent with applicable laws and Manulife/John Hancock policies. To request a reasonable accommodation in the application process, contact hr@manulife.com.

Referenced Salary Location

Toronto, Ontario

Working Arrangement

Hybrid

Salary range is expected to be between

$113,000.00 CAD - $163,000.00 CAD

Employees also have the opportunity to participate in incentive programs and earn incentive compensation tied to business and individual performance. The actual salary will vary depending on local market conditions, geography and relevant job-related factors such as knowledge, skills, qualifications, experience, and education/training. If you are applying for this role outside of the primary location, please contact hr@manulife.com for the salary range for your location.

Manulife offers eligible employees a wide array of customizable benefits, including health, dental, mental health, vision, short- and long-term disability, life and AD&D insurance coverage, adoption/surrogacy and wellness benefits, and employee/family assistance plans. We also offer eligible employees various retirement savings plans (including pension and a global share ownership plan with employer matching contributions) and financial education and counseling resources. Our generous paid time off program in Canada includes holidays, vacation, personal, and sick days, and we offer the full range of statutory leaves of absence. If you are applying for this role in the U.S., please contact hr@manulife.com for more information about U.S.-specific paid time off provisions.

We use data and analytics technologies, such as artificial intelligence (AI), and automated processing tools, to analyze and process the information you provide to us or third parties in the application process. For more information, please refer to our personal information collection statement.

Apply About employer

More about this job

Responsibilities

Build and operate shared Azure Kubernetes Service platform capabilities, including reusable Terraform modules, GitHub Actions workflows, GitOps, and progressive deployment mechanisms. Improve multi-region resilience, disaster recovery, observability, security and compliance automation, and AI-assisted platform tooling with appropriate approval and audit controls.

Requirements

Requires extensive experience in enterprise Azure, production Kubernetes across multiple clusters and regions, progressive delivery, Terraform, reusable GitHub Actions workflows, and production GitOps. Candidates also need hands-on experience with Python, Bash, observability, policy as code, LLM-based operational tooling, and regulated enterprise environments.

Skills

Azure
Kubernetes
Terraform
GitHub Actions
GitOps
Progressive Delivery
Service Mesh
Multi-Region Resilience
Disaster Recovery
Python
Bash
Observability
Policy as Code
Workload Identity
Large Language Models
Model Context Protocol

Benefits

Health Insurance
Dental Insurance
Mental Health Benefits
Vision Insurance
Short-Term Disability Insurance
Long-Term Disability Insurance
Life and Accidental Death and Dismemberment Insurance
Adoption and Surrogacy Benefits
Wellness Benefits
Employee and Family Assistance Plans
Retirement Savings Plans
Pension
Global Share Ownership Plan with Employer Matching
Financial Education and Counseling
Paid Holidays
Vacation
Personal Days
Sick Days
Statutory Leaves of Absence
Incentive Compensation

Visa sponsorship

Not detected in the job text

Keywords

Azure Kubernetes Service
Azure
Terraform
GitHub Actions
GitOps
Flux CD
Argo CD
Istio
Service Mesh
Canary Deployments
Blue-Green Deployments
Multi-Region Resilience
Disaster Recovery
Chaos Engineering
Azure Chaos Studio
Chaos Mesh
Azure Policy
Policy as Code
Workload Identity
OpenID Connect
Observability
New Relic
Datadog
SLI
SLO
Python
Bash
YAML
Large Language Models
Model Context Protocol

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