{"id":2963704,"url":"https://alion.io/job/zurich-insurance-group-machine-learning-engineer","title":"Machine Learning Engineer","company":{"id":1943356,"name":"Zurich Australia","domain":"zurich.com.au","url":"https://alion.io/company/zurich-com-au","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":{"grade":"A","score":93,"open_postings":8,"ghost_share":0,"stale_share":0.5,"repost_share":0,"time_to_fill_p50_days":20,"computed_at":"2026-10-26T05:57:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Toronto, Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":76000,"max_usd":164000,"period":"year","method":"role_country_seniority_unknown","sample_n":229},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CatBoost","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"GitHub","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"Langfuse","optional":false},{"name":"LangGraph","optional":false},{"name":"LightGBM","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Rest API","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"TensorFlow","optional":false},{"name":"Terraform","optional":false},{"name":"XGBoost","optional":false}],"status":"live","first_seen_at":"2026-10-20T23:31:38Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-10-26T01:51:04Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"The Opportunity\nAre 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.\nIf 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.\nZurich 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.\nThis 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.\nYou 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.\nThis is a unique opportunity to build your knowledge and experience for the future in a supportive environment where your voice matters.\nWhat you will do\nAreas of Specialization\nCandidates may align with one area; experience across all four is not expected.\nMachine Learning Engineer - Applied AI\nBuild/productionize predictive ML/AI: classification, regression, forecasting and clustering.\nBuild feature pipelines; establish training, validation/testing and bias/fairness testing.\nEvaluate models; move experiments to production and optimize inference.\nAssess approved A/B, shadow or controlled pilots; monitor data quality, performance/drift and troubleshoot production issues.\nDocument assumptions, limitations, versions, training-data references and approvals.\nRelevant 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.\nMachine Learning Engineer - Generative AI\nEngineer, evaluate and productionize enterprise GenAI using foundation models, retrieval and agentic patterns.\nBuild LLM services and RAG pipelines covering ingestion, parsing, cleaning, chunking, embeddings, indexing/retrieval.\nEvaluate keyword, semantic-vector/hybrid search, filtering/reranking; design prompts, structured outputs/context management.\nImprove groundedness through retrieval quality, grounding checks, citations, abstention and human review.\nBuild controlled agentic workflows/tool integrations; evaluate retrieval, answers, safety, agent behaviour/business outcomes.\nMonitor quality, groundedness, safety, latency, reliability/cost; apply access-aware retrieval, sensitive-data controls and prompt-injection defences.\nMaintain auditable model, prompt, data, index and evaluation records.\nRelevant 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.\nMachine Learning Engineer - Data & ML Platforms\nBuild/operate enterprise-scale data, feature and engineering foundations for ML/AI.\nDevelop training and batch/real-time inference pipelines, reusable feature frameworks and batch/streaming components.\nOptimize Spark/distributed processing; build secure data-platform/AI interfaces and reusable libraries/templates.\nImplement ML dataset quality monitoring; support feature management, lineage/reproducibility.\nEngineer large-scale structured/unstructured data; deploy, monitor and troubleshoot production ML data pipelines.\nRelevant 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.\nMLOps Engineer\nBuild/operate platforms, automation and controls across the ML/AI lifecycle.\nDesign CI/CD for models/AI applications; establish model registration, versioning, lineage/promotion.\nBuild reproducible training/deployment environments; support staged releases, shadow deployments/rollback.\nMonitor health, latency, data quality, model performance/drift; develop retraining/redeployment workflows with validation/approvals.\nManage ML infrastructure/configuration; implement access controls, audit trails/governance evidence and reusable MLOps standards.\nRelevant experience: MLflow, Databricks, Azure Machine Learning, Azure DevOps, GitHub Actions, Python, REST APIs, Docker, Kubernetes, Terraform, model registries and production observability.\nWhat you bring to the table\nAcross the AI Engineering practice, relevant qualifications include:\nPython and SQL skills.\nSoftware engineering fundamentals including Git, APIs, testing and CI/CD.\nUnderstanding of ML concepts and the model-development lifecycle.\nExperience with data processing and analytical workloads.\nExperience in cloud-based development environments.\nUnderstanding of reliable, maintainable, production-ready solutions.\nDepending on your specialization, the following are assets:\nDatabricks, MLflow and Apache Spark/PySpark.\nMicrosoft Azure or equivalent AWS/Google Cloud technologies.\nAzure DevOps or GitHub.\nDocker and containerized applications.\nREST APIs.\nscikit-learn, PyTorch, TensorFlow or comparable ML/deep-learning frameworks.\nAzure OpenAI or comparable managed AI platforms.\nRAG, embeddings, retrieval and LLM evaluation.\nInfrastructure as code, Kubernetes and production observability.\nSecurity, privacy, Responsible AI and model governance in a regulated environment.\nExperience Levels\nJunior\nWork on defined ML/AI engineering tasks with guidance.\nWrite reliable Python and SQL.\nDevelop/test components within established architecture and engineering patterns.\nUnderstand core ML and software-engineering practices.\nParticipate in code reviews and troubleshooting.\nLearn Zurich's AI engineering, security and governance standards.\nIntermediate\nIndependently design/deliver ML/AI components and solutions.\nOwn development through production implementation.\nMake appropriate engineering and technology choices.\nTroubleshoot complex data, model and production issues.\nContribute to architecture and engineering standards.\nMentor junior team members.\nWork with stakeholders to refine technical requirements.\nSenior\nLead design/engineering of complex enterprise AI/ML solutions.\nMake architecture and technology decisions.\nEstablish reusable patterns and engineering standards.\nLead production-readiness, scalability, reliability and security decisions.\nMentor engineers and provide technical leadership.\nDrive engineering excellence through design/code reviews.\nPartner with architecture, security, governance and business leadership.\nEvaluate emerging AI technologies and appropriate enterprise adoption patterns.\nCompensation\nAt Zurich Canada, we are committed to pay equity. We determine compensation based on objective criteria such as skills, experience, and internal equity. The salary range for this position is $92,3000- $151,400 CAD.\nOur Culture\nAt Zurich, we are proud of our culture. We are passionate about Diversity, Inclusion, Equity and Belonging (DIEB). We want you to bring your whole self to work, and we want our employees to be reflective of the communities in which we live and work.\nWe value diverse perspectives and foster a collaborative environment where employees are empowered to make an impact. We offer a comprehensive rewards package designed to support employee well-being, growth, and success.\nComprehensive health and wellness benefits.\nMinimum four weeks of vacation annually.\nFour personal days each year to support personal, cultural, religious, or well-being needs.\nAccess to ongoing learning and development opportunities.\nOpportunities to support and give back to our communities.\nRecognition as one of Greater Toronto's Top Employers and recipient of Insurance Business Canada's 5-Star Diversity, Equity and Inclusion Award.\nMake a difference. Be challenged. Be inspired. Be supported. Love what you do. Work for us.\nA future with Zurich\nNow is the time to move forward and make a difference. At Zurich, we want you to share your unique perspectives, experiences and ideas so we can grow and drive sustainable change together. As part of a leading global organization, Zurich North America has over 148 years of experience managing risk and supporting resilience. We are a leading provider of commercial property-casualty insurance solutions and a wide range of risk management products and services for businesses and individuals. Today, we serve more than 25 industries, from agriculture to technology and insure 90% of the Fortune 500®. Our growth strategy is not limited to our business. As an employer, Zurich strives to provide ongoing career development opportunities and foster an environment where voices are diverse, behaviors are inclusive, actions drive equity, and our people feel a sense of belonging. Be a part of the next evolution of the insurance industry. Join us in building a brighter future for our people, our customers and the communities we serve.\nAs a global company, Zurich recognizes the diversity of our workforce as an asset. We recruit talented people from a variety of backgrounds with unique perspectives that are truly welcome here. Taken together, diversity and inclusion bring us closer to our common goal: exceeding our customers’ expectations. Zurich is committed to providing a diverse, inclusive and barrier-free environment resulting in an accessible organization for employees, customers, and other parties who interact with, or on behalf of, Zurich. We strive to achieve a workplace free of discrimination of all forms, including discrimination on the basis of physical or mental disability, or medical condition. If you are interested in a job opportunity, please advise if you require an accommodation, so we can work with you to provide a more accessible process.\nZurich does not accept unsolicited resumes from search firms or employment agencies. Any unsolicited resume will become the property of Zurich Canadian Holdings Limited. If you are a preferred vendor, please use our Recruiting Agency Portal for resume submission.\nOnly applicable for Canada: For Zurich Canada's commitment to diversity and accessibility pleaseclick here.If you would prefer to not receive future electronic messages from Zurich Insurance Company Ltd's recruitment management system, please email your request to have your job profile deleted from the system by clickinghere. 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