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 qualified 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 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 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 " #J-18808-Ljbffr

📌 Ai And Machine Learning Engineer (Ottawa)
🏢 Jobtailor
📍 Ottawa