- 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 workplace 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

📌 AI & Machine Learning Engineer, Level 17 or 18 (Ottawa)
🏢 Jobtailor
📍 Ottawa