Machine Learning Engineer

About This Role

Making a difference in the world's most impactful global insurance provider, Zurich Canada, is the core of this Machine Learning / AI Engineer role. You will design, develop, and operationalize scalable AI and machine learning solutions within a modern cloud data and AI ecosystem. This opportunity covers positions across Junior, Intermediate, and Senior experience levels, allowing you to build knowledge and experience in a supportive environment.

Responsibilities

• Build/productionize predictive ML/AI: classification, regression, forecasting and clustering
• Build feature pipelines; establish training, validation/testing and bias/fairness testing
• Evaluate models; move experiments to production and optimize inference
• 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
• 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
• Evaluate keyword, semantic-vector/hybrid search, filtering/reranking; design prompts, structured outputs/context management
• Improve groundedness through retrieval quality, grounding checks, citations, abstention and human review
• Build controlled agentic workflows/tool integrations; evaluate retrieval, answers, safety, agent behaviour/business outcomes
• 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
• 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
• Optimize Spark/distributed processing; build secure data-platform/AI interfaces and reusable libraries/templates
• Implement ML dataset quality monitoring; support feature management, lineage/reproducibility
• Engineer large-scale structured/unstructured data; deploy, monitor and troubleshoot production ML data pipelines
• Build/operate platforms, automation and controls across the ML/AI lifecycle
• Design CI/CD for models/AI applications; establish model registration, versioning, lineage/promotion
• Build reproducible training/deployment environments; support staged releases, shadow deployments/rollback
• Monitor health, latency, data quality, model performance/drift; develop retraining/redeployment workflows with validation/approvals