AI and Machine Learning Engineer

Jobtailor; Ottawa; On-site; CAD 120,000 - 180,000; Full time

Job summary

Jobtailor is seeking an experienced AI/ML engineer to design and deploy production-ready machine learning and generative AI solutions. You will collaborate with data scientists, architects, platform, and security teams to transform research models into reliable, scalable services.

The role involves full lifecycle management of AI systems, including versioning, monitoring, retraining, scaling, rollback, and decommissioning, plus implementing RAG and agentic models with strong governance and

Qualifications

• University degree in STEM field.
• 5+ years in AI/ML engineering with production systems.
• 7+ years designing large-scale data platforms (Databricks & Azure).
• 3+ years with production ML/AI systems.
• 3+ years on Azure cloud platforms, Databricks, and security.
• 3+ years in CI/CD and infrastructure as code for AI.
• 3+ years coding in Python and data languages (SQL, Java, Scala).
• 7+ years in IT service management and Agile implementation.
• 3+ years in generative AI or MLOps in production.

Responsibilities

• Design and deploy production-ready ML and generative AI solutions.
• Collaborate with data scientists, architects, and security teams to productionize models.
• Manage lifecycle of AI systems including versioning, monitoring, retraining, and scaling.
• Implement RAG-based generative AI apps and agentic models.
• Incorporate security, governance, and risk management controls.
• Automate deployments with CI/CD and infrastructure as code.
• Monitor performance, data/model drift, costs, and incidents.
• Prepare data with stakeholders and clients.

Skills

Collaboration

Problem-solving

Communication

Bilingual English/French

Education

University degree in CS/Engineering/Math/Data Science or related

Tools

Databricks

Azure AI Foundry

Git

Containerization

CI/CD

Python

SQL

Java

Scala

Azure

Job description

• 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 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

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