Do you have the ambition and desire to be the best and thrive at the most impactful global insurance provider in the world? If you have experience in machine learning, artificial intelligence or ML/AI engineering and are looking for a new challenge, we would love to hear from you. Zurich Canada is expanding its AI Engineering capability and is looking for Machine Learning / AI Engineers to design, develop and operationalize scalable AI and machine learning solutions. This posting covers four positions across multiple experience levels, including Junior, Intermediate and Senior opportunities. Candidates will be considered for the appropriate level and specialization based on their experience, technical depth, demonstrated capabilities and scope of responsibility. You will work within a contemporary cloud data and AI ecosystem and collaborate with Data Engineering, Data Science, Architecture, Governance, Security and business teams to turn data and AI opportunities into secure, reliable and production-ready solutions. Machine Learning Engineer
- Applied AI Build/productionize predictive ML/AI: classification, regression, forecasting and clustering. Build feature pipelines; establish training, validation/testing and bias/fairness testing. Assess approved A/B, shadow or controlled pilots; monitor data quality, performance/drift and troubleshoot production issues. Document assumptions, limitations, versions, training-data references and approvals. Relevant

experience:
statistical ML; Machine Learning Engineer
- Generative AI Engineer, evaluate and productionize enterprise GenAI using foundation models, retrieval and agentic patterns. Build LLM services and RAG pipelines covering ingestion, parsing, cleaning, chunking, embeddings, indexing/retrieval.



Improve groundedness through retrieval quality, grounding checks, citations, abstention and human review. Build controlled agentic workflows/tool integrations; Monitor quality, groundedness, safety, latency, reliability/cost; apply access-aware retrieval, sensitive-data controls and prompt-injection defences. Maintain auditable model, prompt, data, index and evaluation records. Machine Learning Engineer
- Data & ML Platforms Build/operate enterprise-scale data, feature and engineering foundations for ML/AI. Develop training and batch/real-time inference pipelines, reusable feature frameworks and batch/streaming components. build secure data-platform/AI interfaces and reusable libraries/templates. Implement ML dataset quality monitoring; Engineer large-scale structured/unstructured data; deploy, monitor and troubleshoot production ML data pipelines. Databricks, Spark/PySpark, Delta Lake, Python, SQL, batch/streaming architectures, feature management, data-quality testing, lineage, cloud storage/compute, CI/CD and infrastructure as code. MLOps Engineer Build/operate platforms, automation and controls across the ML/AI lifecycle. Design CI/CD for models/AI applications; Build reproducible training/deployment environments; support staged releases, shadow deployments/rollback. Monitor health, latency, data quality, model performance/drift; Manage ML infrastructure/configuration; implement access controls,



audit trails/governance evidence and reusable MLOps standards. MLflow, Databricks, Azure Machine Learning, Azure DevOps, GitHub Actions, Python, REST APIs, Docker, Kubernetes, Terraform, model registries and production observability. What you bring to the tableAcross the AI Engineering practice, relevant qualifications include: Python and SQL skills. Software engineering fundamentals including Git, APIs, testing and CI/CD. Understanding of ML concepts and the model-development lifecycle. Experience with data processing and analytical workloads. Depending on your specialization, the following are assets: Databricks, MLflow and Apache Spark/PySpark. Microsoft Azure or equivalent AWS/Google Cloud technologies. Azure DevOps or GitHub. scikit-learn, PyTorch, TensorFlow or comparable ML/deep-learning frameworks. Azure OpenAI or comparable managed AI platforms. Security, privacy, Responsible AI and model governance in a regulated environment. Work on defined ML/AI engineering tasks with guidance. Write reliable Python and SQL. Develop/test components within established architecture and engineering patterns. Understand core ML and software-engineering practices. Learn Zurich's AI engineering, security and governance standards. Independently design/deliver ML/AI components and solutions. Own development through production implementation. Troubleshoot complex data, model and production issues.
Work with stakeholders to refine technical requirements. Senior Lead design/engineering of complex enterprise AI/ML solutions. Mentor engineers and provide technical leadership. Evaluate emerging AI technologies and appropriate enterprise adoption patterns. CompensationAt Zurich

📌 Machine Learning Engineer (Winnipeg)
🏢 Zurich Canada
📍 Winnipeg