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Wawanesa Insurance

Machine Learning Operations Engineer/Senior

Toronto, ON, Canada
AB, Canada
Winnipeg, MB, Canada
Ottawa, ON, Canada
Kitchener, ON, Canada
Calgary, AB, Canada
Edmonton, AB (Hybrid), AB, Canada
North York - Toronto, ON (Hybr, ON, Canada

Experience: More than 5 years of experience in developing and deploying enterprise-scale machine learning solutions.
Working hours: 15 hours per week
Posting language: English
Posted: Sep 29, 2026

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Original job description

Job ID: 10373

Employment Type: Existing Role

Work Environment: We offer a hybrid work environment that offers flexibility to our employees in balancing in-office (2 days per week OR 15 hours per week in a Wawanesa office) and remote work. You may work from any of the following locations: Winnipeg, MB; Calgary, AB; Edmonton, AB; Toronto (North York), ON; Kitchener, ON; Ottawa, ON.

Working Business Language: English

Salary: At Wawanesa, salary is only one component of a holistic, comprehensive and competitive offering that we provide to our employees. In addition to salary, full-time and part-time permanent employees are eligible for an annual bonus plan, leave of absence top-up programs and provided with generous vacation time, personal days, premium free benefits and pension plan.

The salary offered for this role is determined with consideration to various factors, including but not limited to: your work location, local labour market conditions, external market salary data, internal pay equity and the knowledge, skills, experience and anticipated proficiency in the role. The salary offered is estimated to be within the following range: $110,000 - $160,000. Candidates with salary expectations outside of the range are still encouraged to apply.

About The Wawanesa Mutual Insurance Company
Founded in 1896, The Wawanesa Mutual Insurance Company is one of Canada’s largest mutual insurers, 100% owned by its members, with more than $4.1 billion in annual revenue and $12.5 billion in assets. Headquartered in Winnipeg, Wawanesa is the parent company of Wawanesa Life, which provides life insurance solutions throughout Canada, and Western Financial Group, a leading national distributor of personal and business insurance. In March of 2026, Wawanesa entered into an agreement to acquire Everest Insurance Company of Canada to strengthen its commercial insurance capabilities and advance its long-term growth strategy.

Wawanesa proudly serves more than 1.8 million members and we are home to more than 3,000 employees across Canada. The company actively gives back to organizations that strengthen communities, donating more than $4 million annually to charitable organizations, including more than $2 million each year in support of people on the front lines of climate change. Learn more at wawanesa.com.

We are currently looking for dedicated, driven, and enthusiastic individuals who thrive in an environment that welcomes change and are looking for an opportunity for diverse experience and advancement on a growing team.

Job Overview

The Machine Learning Operations Engineer/Senior is responsible for the design, development, deployment, monitoring, and operationalization of machine learning and AI solutions. The engineer works closely with Business Stakeholders, Data Analysts, Data Scientists, and ML Engineers within Analytics Exploration to transform business opportunities into scalable, production-grade AI and machine learning solutions. The role collaborates with Software Engineers, Data Engineers, Platform Engineers, and Enterprise Architecture teams to ensure robust infrastructure, governed data pipelines, and secure MLOps practices that enable reliable deployment and management of AI and machine learning models across the enterprise.

Job Responsibilities

Partner with Business Stakeholders and Analytics Exploration teams to identify, prioritize, and operationalize AI and machine learning use cases that deliver measurable business value.
Design, implement, and maintain enterprise MLOps frameworks supporting model training, deployment, monitoring, governance, and lifecycle management.
Develop and support machine learning solutions using Databricks, MLflow, Unity Catalog, Feature Store, and other cloud-native AI platforms.
Implement model monitoring capabilities including data drift, model drift, performance monitoring, explainability, and alerting.
Establish CI/CD and automated deployment pipelines for machine learning and generative AI solutions.
Ensure adherence to Model Risk Management (MRM), Responsible AI, data governance, security, and regulatory compliance requirements.
Collaborate with Platform Engineering and Architecture teams to define standards, patterns, and best practices for AI/ML solution deployment.
Support the evaluation and adoption of emerging AI technologies, including Generative AI, LLMOps, Agentic AI, and advanced analytics capabilities.

Qualifications

More than 5 years of experience in developing and deploying enterprise-scale machine learning solutions.
Hands-on experience with Databricks Lakehouse Platform, including Databricks Workflows, MLflow, Unity Catalog, Model Serving, Feature Store, and Mosaic AI capabilities.
Experience implementing end-to-end MLOps solutions on Databricks, AWS SageMaker, or comparable enterprise machine learning platforms.
Knowledge of model governance, model lifecycle management, Responsible AI practices, and Model Risk Management (MRM) frameworks.
Experience building and managing CI/CD pipelines for machine learning workloads using tools such as GitHub Actions, Azure DevOps, Jenkins, or equivalent.
Experience monitoring machine learning models for data drift, concept drift, model performance degradation, and operational reliability.
Familiarity with Generative AI, Large Language Models (LLMs), Retrieval Augmented Generation (RAG), prompt engineering, and LLMOps practices.
Experience working with cloud-based data and analytics platforms, including AWS services such as S3, Glue, Lambda, SageMaker, EKS, and related technologies.
Strong understanding of data governance, security, access controls, metadata management, and auditability within enterprise AI platforms.
Experience collaborating directly with Business Stakeholders to translate business objectives into AI and machine learning solutions.
Experience working within cross-functional teams including Data Science, Analytics Exploration, Data Engineering, Software Engineering, Platform Operations, and Architecture.
Experience implementing enterprise AI platforms using Databricks on AWS.
Experience supporting insurance, financial services, or other highly regulated industries.
Experience with GenAI, Agentic AI, RAG architectures, vector databases, and AI governance frameworks.
Experience establishing enterprise MLOps standards, monitoring frameworks, and operational support models.
Advanced degree in Data Science, Computer Science, Artificial Intelligence, Machine Learning, Statistics, or a related field

Diversity Equity, Inclusion & Belonging

At Wawanesa, we are committed to Diversity, Equity, Inclusion and Belonging (DEIB) and believe that our strength lies in the diversity of our people – this is supported by having a representative workforce.

We welcome applications from all qualified candidates, including racialized persons, women, Indigenous Peoples, persons with disabilities, members of the 2SLGBTQIA+ community, gender-diverse and neurodiverse individuals, and anyone who can contribute to the further diversification of thought and ideas.

We aim to ensure our recruitment process is accessible to all candidates. If you require accommodations during any stage of the recruitment process, please reach out in confidence to jobs@wawanesa.com.

All Wawanesa job applicants are subject to Wawanesa's Privacy Policy.

Please note that the recruitment process for this position may involve the use of AI tools to screen, assess, or select applicants. All final decisions are taken or reviewed by human recruiters and human hiring leaders in compliance with all applicable legislation.

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More about this job

Responsibilities

The engineer is responsible for designing, deploying, and monitoring scalable machine learning and AI solutions while collaborating with cross-functional teams. They will establish MLOps frameworks, manage model lifecycles, and ensure adherence to data governance and security standards.

Requirements

Candidates must have over 5 years of experience in enterprise-scale machine learning and hands-on expertise with the Databricks Lakehouse platform. Proficiency in MLOps, model governance, CI/CD pipelines, and cloud-based data platforms is required.

Skills

Machine Learning Operations
Databricks
MLflow
Unity Catalog
Feature Store
CI/CD
Model Governance
Generative AI
LLMOps
AWS
Data Drift Monitoring
Model Risk Management
Python
Cloud-native AI platforms
Data Governance
Agentic AI

Benefits

Annual bonus plan
Leave of absence top-up programs
Generous vacation time
Personal days
Premium free benefits
Pension plan

Visa sponsorship

Not detected in the job text

Keywords

Machine Learning
MLOps
Databricks
MLflow
Unity Catalog
Feature Store
AWS
Generative AI
LLMOps
RAG
Model Risk Management
CI/CD
Data Governance
Cloud Computing
Agentic AI
Data Science
Artificial Intelligence
Mosaic AI
GitHub Actions
Azure DevOps
Jenkins
S3
Glue
Lambda
EKS
Metadata Management
Insurance
Financial Services
Vector Databases
Responsible AI

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