Design and maintain reusable ML assets, including feature pipelines, shared components, deployment patterns, and evaluation frameworks Collaborate with data scientists, architects, platform, and security teams to transition models from research to scalable, reliable production services Engineer and deploy production-grade ML and GenAI solutions using batch, real-time, and event-driven inference patterns Own or support the AI system lifecycle, including MLOps, LLMOps, AgentOps, versioning, monitoring, retraining, scaling, rollback, and retirement Operationalize RAG-based and agentic GenAI applications with evaluation, guardrails, and cost awareness Embed security, governance, Responsible AI controls, Protected B requirements, auditability, and risk-based controls Automate AI delivery through CI/CD pipelines, Infrastructure as Code, and standardized environment promotion Monitor, diagnose, and remediate system health, model and data drift, bias indicators, cost anomalies, and production incidents within SLAs Build and validate predictive, descriptive, behavioural, and structured-data machine learning models Partner with business stakeholders and SMEs to translate insights into actionable recommendations Apply engineering judgment to balance performance, scalability, cost, security, and risk Provide fault isolation, initial resolution, concepts, and prototypes for AI product and service ideas Requirements

University degree in Computer Science, Engineering, Mathematics, Data Science, or a related technical discipline Level 17: Minimum 5 years of experience in AI and ML engineering roles delivering production systems in enterprise environments Level 17:



Minimum 5 years of experience designing or contributing to large-scale data platforms supporting batch and real-time workloads, primarily using Databricks and Azure Level 17: Minimum 3 years of hands-on experience building and operating ML or AI systems in production, including monitoring, retraining, and incident response Level 17: Minimum 3 years of experience with Azure cloud deployment, automation, networking, and security services, with focus on Databricks data operations Level 17: Minimum 3 years of experience implementing CI/CD pipelines and Infrastructure as Code for ML/AI workloads Level 17: Minimum 3 years of experience developing production-grade code using Python and data-centric languages such as SQL, Java, or Scala Level 17: Minimum 3 years of experience in formal IT service management and Agile delivery environments Level 17: Minimum 1 year of applied GenAI or MLOps experience, including LLMs, RAG-based architectures, or agentic/workflow-oriented patterns in production Level 18: Minimum 7 years of experience in AI and ML engineering roles delivering production systems in enterprise environments Level 18: Minimum 7 years of experience designing or contributing to large-scale data platforms supporting batch and real-time workloads, primarily using Databricks and Azure Level 18:



Minimum 7 years of hands-on experience building and operating ML or AI systems in production Level 18: Minimum 7 years of experience with Azure cloud deployment, automation, networking, and security services Level 18: Minimum 7 years of experience implementing CI/CD pipelines and Infrastructure as Code for ML/AI workloads Level 18: Minimum 5 years of experience developing production-grade code using Python and data-centric languages Level 18: Minimum 7 years of experience in formal IT service management and Agile delivery environments Level 18: Minimum 3 years of applied GenAI or MLOps experience in production Candidates must be able to work legally in Canada at the time of application Candidates must meet government security screening requirements Core Competencies

Demonstrates expertise in designing and maintaining production-grade ML and GenAI solutions, with a robust focus on MLOps, CI/CD automation, and Azure cloud services. Proven ability to collaborate across teams to operationalize AI systems while ensuring security, governance, and performance. Highest-signal resume keywords

MLOps GenAI CI/CD Pipelines Azure Cloud Deployment Databricks Hard Skills

Machine Learning AI Engineering Python SQL Java Scala Infrastructure as Code Data Platform Design Monitoring and Incident Response Feature Pipeline Development Soft Skills

Collaboration Problem Solving Communication Engineering Judgment Stakeholder Engagement Industry Keywords

Responsible AI Auditability Risk-Based Controls Agile Delivery IT Service Management Tools & Technologies

Databricks Azure CI/CD Tools Event-Driven Inference Monitoring Tools

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📌 AI & Machine Learning Engineer, Level 17 or 18 (Montreal)
🏢 Jobtailor
📍 Montreal