Building Science Engineer Jobs in Quebec (NOW HIRING)

• 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

• 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

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.

• MLOps


• GenAI


• CI/CD Pipelines


• Azure Cloud Deployment


• Databricks

• Machine Learning


• AI Engineering


• Python


• SQL


• Java


• Scala


• Infrastructure as Code


• Data Platform Design


• Monitoring and Incident Response


• Feature Pipeline Development

• Collaboration


• Problem Solving


• Communication


• Engineering Judgment


• Stakeholder Engagement

• Responsible AI


• Auditability


• Risk-Based Controls


• Agile Delivery


• IT Service Management

• Databricks


• Azure


• CI/CD Tools


• Event-Driven Inference


• Monitoring Tools