Design and maintain reusable machine learning assets, including feature pipelines, shared components, deployment templates, and evaluation frameworksCollaborate with data scientists, architects, and platform and security teams to transform research models into reliable, scalable production servicesDesign and deploy production-ready machine learning and generative AI solutionsManage or support the full lifecycle of AI systems, including versioning, monitoring, retraining, scaling, rollback, and decommissioningImplement generative AI applications based on retrieval-augmented generation (RAG) and agentic modelsIntegrate security, governance, responsible AI, auditability, and risk management controlsAutomate deployments using CI/CD practices, infrastructure as code, and standardized environmentsMonitor, diagnose, and resolve performance issues, data and model drift, anomalies, costs, and production incidentsDesign and validate predictive, descriptive, and behavioral models related to operational performance indicatorsDesign supervised and unsupervised learning algorithms for structured dataPrepare data in collaboration with stakeholders and internal clientsDevelop concepts and prototypes for recent AI products and servicesBalance performance, scalability, cost, security, and risk management in enterprise AI systemsRequirements
University degree in computer science, engineering, mathematics, data science, or a related technical disciplineLevel 17: At least 5 years of experience in AI or machine learning engineering involving production systemsLevel 17: At least 5 years of experience designing large-scale data platforms, primarily Databricks and AzureLevel 17: At least 3 years of hands-on experience with production machine learning or AI systemsLevel 17: At least 3 years of experience with Azure cloud platforms, deployment, automation, networking, and security, with a focus on DatabricksLevel 17:



At least 3 years of experience with continuous integration, continuous deployment, and infrastructure as code for AILevel 17: At least 3 years of experience developing production-ready code using Python and data-focused languages, including SQL, Java, or ScalaLevel 17: At least 3 years of experience with formal IT service management and Agile implementationLevel 17: At least 1 year of hands-on experience with generative AI or machine learning operations in productionLevel 18: At least 7 years of experience in AI or machine learning engineering involving production systemsLevel 18: At least 7 years of experience designing large-scale data platforms, primarily Databricks and AzureLevel 18: At least 7 years of hands-on experience with production machine learning or AI systemsLevel 18: At least 7 years of experience with Azure cloud platforms and Databricks data operationsLevel 18: At least 7 years of experience with continuous integration, continuous deployment, and infrastructure as code for AILevel 18: At least 5 years of experience developing production-ready code using Python and SQL, Java, or ScalaLevel 18: At least 7 years of experience with IT service management and Agile implementationLevel 18: At least 3 years of hands-on experience with generative AI or machine learning operations in productionFluency in both of Canada’s official languages:



English and FrenchCandidates must meet government security requirementsExperience deploying containerized AI workloads and scalable cloud infrastructureKnowledge of AI security, governance, and risk management frameworksKnowledge of or experience with MITRE ATLAS, MITRE ATT&CK;, OWASP LLM Top 10, OWASP ML Top 10, ISO/IEC 42001, ISO/IEC 27001, NIST 800-53, HITRUST, ENISA, and the EU AI ActLevel 18: Hands‑on experience with Databricks and Azure AI Foundry, model lifecycle management, and Git workflowsLevel 18: Experience delivering production-ready generative AI solutions, including RAG and agentic modelsLevel 18: Experience with model and large language model evaluation, telemetry, and feedback loopsLevel 18: Experience collaborating with external vendors on managed services or professional services projectsCanadian citizenship or permanent residency is preferred to work legally in Canada at the time of applicationCore Competencies
Demonstrates expertise in designing and deploying production-ready machine learning and generative AI solutions, with a strong focus on Azure and Databricks platforms. Proficient in managing the full lifecycle of AI systems, including automation, security, and compliance with industry standards.Highest-signal resume keywords
Machine Learning EngineeringAzure Cloud PlatformsDatabricks Data OperationsGenerative AI SolutionsContinuous Integration/Continuous DeploymentATS Optimization Keywords
Hard Skills
PythonSQLJavaScalaMachine Learning AlgorithmsData PreparationModel EvaluationInfrastructure as CodeCI/CD PracticesAI System MonitoringSoft Skills
CollaborationProblem-SolvingCommunicationIndustry Keywords
AI SecurityGovernance FrameworksRisk ManagementMITRE ATLASISO/IEC 27001NIST 800-53EU AI ActHITRUSTOperational Performance IndicatorsRetrieval-Augmented GenerationTools & Technologies
DatabricksAzure AI FoundryGitContainerizationAgile Methodologies"#J-18808-Ljbffr

📌 Ai And Machine Learning Engineer (Riviere-des-Prairies—Pointe-aux-Trembles)
🏢 Jobtailor
📍 Riviere-des-Prairies—Pointe-aux-Trembles