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 setting 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 strong 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 (Quebec City)
🏢 Jobtailor
📍 Quebec City