Machine Learning Engineer

The Opportunity Are you looking for a caring, collaborative, values-driven workplace with inspiring teammates and leaders? Do you have the ambition and desire to be the best and thrive at the most impactful global insurance provider in the world? Look no further than Zurich Canada. 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 modern 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. This is a unique opportunity to build your knowledge and experience for the future in a supportive environment where your voice matters. What you will do Areas of Specialization Candidates may align with one area; experience across all four is not expected. 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. • 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. Relevant experience: statistical ML; scikit-learn, PyTorch, TensorFlow, XGBoost, LightGBM or CatBoost; feature engineering; model evaluation; time-based validation; leakage prevention; imbalanced datasets; calibration; experiment tracking; batch/real-time inference. 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. • 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. Relevant experience: Azure OpenAI or comparable platforms; RAG; embeddings; vector indexes; BM25; semantic/hybrid search; retrieval/LLM evaluation; prompt engineering; FastAPI; LangGraph, LangChain, Semantic Kernel, Langfuse or OpenTelemetry. 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. • 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. Relevant experience: 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; 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. • Manage ML infrastructure/configuration; implement access controls, audit trails/governance evidence and reusable MLOps standards. Relevant experience: 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 table Across 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. • Experience in cloud-based development environments. • Understanding of reliable, maintainable, production-ready solutions. 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. • Docker and containerized applications. • REST APIs. • scikit-learn, PyTorch, TensorFlow or comparable ML/deep-learning frameworks. • Azure OpenAI or comparable managed AI platforms. • RAG, embeddings, retrieval and LLM evaluation. • Infrastructure as code, Kubernetes and production observability. • Security, privacy, Responsible AI and model governance in a regulated environment. Experience Levels Jun