Design and maintain reusable machine learning assets, including feature pipelines, shared components, deployment templates, and evaluation frameworks
Collaborate with data scientists, architects, and platform and security teams to transform research models into reliable, scalable production services
Design and deploy production-ready machine learning and generative AI solutions
Manage or support the full lifecycle of AI systems, including versioning, monitoring, retraining, scaling, rollback, and decommissioning
Implement generative AI applications based on retrieval-augmented generation (RAG) and agentic models
Integrate security, governance, responsible AI, auditability, and risk management controls
Automate deployments using CI/CD practices, infrastructure as code, and standardized environments
Monitor, diagnose, and resolve performance issues, data and model drift, anomalies, costs, and production incidents
Design and validate predictive, descriptive, and behavioral models related to operational performance indicators
Design supervised and unsupervised learning algorithms for structured data
Prepare data in collaboration with stakeholders and internal clients
Develop concepts and prototypes for new AI products and services
Balance performance, scalability, cost, security, and risk management in enterprise AI systems
Requirements University degree in computer science, engineering, mathematics, data science, or a related technical discipline
Level 17: At least 5 years of experience in AI or machine learning engineering involving production systems
Level 17: At least 5 years of experience designing large-scale data platforms, primarily Databricks and Azure
Level 17: At least 3 years of hands-on experience with production machine learning or AI systems
Level 17: At least 3 years of experience with Azure cloud platforms, deployment, automation, networking, and security, with a focus on Databricks
Level 17:



At least 3 years of experience with continuous integration, continuous deployment, and infrastructure as code for AI
Level 17: At least 3 years of experience developing production-ready code using Python and data-focused languages, including SQL, Java, or Scala
Level 17: At least 3 years of experience with formal IT service management and Agile implementation
Level 17: At least 1 year of hands-on experience with generative AI or machine learning operations in production
Level 18: At least 7 years of experience in AI or machine learning engineering involving production systems
Level 18: At least 7 years of experience designing large-scale data platforms, primarily Databricks and Azure
Level 18: At least 7 years of hands-on experience with production machine learning or AI systems
Level 18: At least 7 years of experience with Azure cloud platforms and Databricks data operations
Level 18: At least 7 years of experience with continuous integration, continuous deployment, and infrastructure as code for AI
Level 18: At least 5 years of experience developing production-ready code using Python and SQL, Java, or Scala
Level 18: At least 7 years of experience with IT service management and Agile implementation
Level 18: At least 3 years of hands-on experience with generative AI or machine learning operations in production
Fluency in both of Canada’s official languages: English and French
Candidates must meet government security requirements
Experience deploying containerized AI workloads and scalable cloud infrastructure
Knowledge of AI security,



governance, and risk management frameworks
Knowledge 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 Act
Level 18: Hands‑on experience with Databricks and Azure AI Foundry, model lifecycle management, and Git workflows
Level 18: Experience delivering production-ready generative AI solutions, including RAG and agentic models
Level 18: Experience with model and large language model evaluation, telemetry, and feedback loops
Level 18: Experience collaborating with external vendors on managed services or professional services projects
Canadian citizenship or permanent residency is preferred to work legally in Canada at the time of application
Core Competencies Demonstrates expertise in designing and deploying production-ready machine learning and generative AI solutions, with a solid 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 Engineering
Azure Cloud Platforms
Databricks Data Operations
Generative AI Solutions
Continuous Integration/Continuous Deployment
ATS Optimization Keywords Hard Skills Python
SQL
Java
Scala
Machine Learning Algorithms
Data Preparation
Model Evaluation
Infrastructure as Code
CI/CD Practices
AI System Monitoring
Soft Skills Collaboration
Problem-Solving
Communication
Industry Keywords AI Security
Governance Frameworks
Risk Management
MITRE ATLAS
ISO/IEC 27001
NIST 800-53
EU AI Act
HITRUST
Operational Performance Indicators
Retrieval-Augmented Generation
Tools & Technologies Databricks
Azure AI Foundry
Git
Containerization
Agile Methodologies
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📌 AI and Machine Learning Engineer (Quebec City)
🏢 Jobtailor
📍 Quebec City