Machine Learning Operations Engineer/Senior 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.Working Business Language : English 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.

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.

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.

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.

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. 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. Robust 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 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.

If you require accommodations during any stage of the recruitment process, please reach out in confidence to [email protected] note that the recruitment process for this position may involve the use of AI tools to screen, assess, or select applicants.

📌 Senior Machine Learning Operations Engineer - Remote (Winnipeg)
🏢 Wawanesa
📍 Winnipeg