AI & Machine Learning Engineer, Level 17 or 18

Jobtailor; Ottawa; On-site; CAD 130,000 - 180,000; Full time

Job summary

Jobtailor in Ottawa, Canada seeks an experienced AI/ML Engineer to design and deploy production‑grade ML/GenAI solutions, focusing on MLOps, Databricks, and Azure.

You will collaborate with data scientists, platform and security teams to transition models from research to scalable, reliable production services, owning the AI system lifecycle including CI/CD, monitoring, retraining and governance.

Qualifications

• University degree in Computer Science, Engineering, Mathematics, Data Science, or a related technical discipline.
• Level 17/18 equivalent: 5+ to 7+ years of AI/ML production experience in enterprise settings.
• Hands-on production ML/AI with Azure, Databricks, and CI/CD for ML workloads.
• Experience designing/operating large-scale data platforms with batch and real-time workloads.
• Strong experience building production-grade code in Python and data-centric languages.

Responsibilities

• Design and maintain reusable ML assets: feature pipelines, components, deployment patterns.
• Collaborate with data scientists, platform, and security teams to productionize models.
• Engineer and deploy production-grade ML/GenAI solutions using batch, real-time, and event-driven inference.
• Own AI system lifecycle: MLOps, LLMOps, versioning, monitoring, retraining, and scaling.
• Operationalize RAG-based GenAI apps with guardrails and cost awareness.
• Embed governance, Responsible AI controls, and risk-based controls.
• Automate AI delivery via CI/CD, IaC, and environment promotion.
• Monitor health, data/model drift, bias indicators, and production incidents within SLAs.
• Build and validate predictive, descriptive, behavioural ML models.
• Translate insights into actionable recommendations with business stakeholders.

Skills

MLOps

GenAI

CI/CD pipelines

Azure Cloud Deployment

Databricks

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

Governance

Education

University degree in Computer Science, Engineering, Mathematics, Data Science, or a related technical discipline

Tools

Databricks

Azure

CI/CD Tools

Event-Driven Inference

Monitoring Tools

Infrastructure as Code

Job description

• 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

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