- 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 robust 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 (Ottawa)
🏢 Jobtailor
📍 Ottawa