Azure ML Ops – Technical Lead Solution Architect

Mississauga - Canada

Monthly Salary: Not Disclosed

Experience Required: 7-10years

Posted on: 07-11-2025

Vacancies: 1 Vacancy

Job Summary

We are seeking a seasoned Azure ML Ops Engineer to lead the design deployment and operationalization of machine learning solutions on the Azure platform. The ideal candidate will have deep expertise in MLOps practices CI/CD for ML pipelines and a strong understanding of Azure ML DevOps and containerized deployments. This role is critical to ensuring scalable secure and reliable ML model delivery in a global enterprise environment.

Key Responsibilities

Design and implement CI/CD pipelines for ML model training testing and deployment using Azure DevOps and Azure ML.
Manage the end-to-end lifecycle of ML models including versioning monitoring and retraining.
Deploy and manage models using Azure ML endpoints AKS and containerized environments (Docker/Kubernetes).
Collaborate with data scientists data engineers and architects to productionize ML solutions.
Ensure compliance with enterprise standards for security governance and auditability.
Monitor model performance detect drift and implement automated retraining workflows.
Optimize infrastructure for cost scalability and performance.
Maintain documentation and knowledge repositories for MLOps best practices.

Requirements

Required Qualifications

Bachelors or masters degree in computer science Engineering or a related field.
8 years of experience in IT with at least 3 years in MLOps or ML engineering roles.
Proficiency in Azure ML Azure DevOps and cloud-native services.
Strong programming skills in Python and experience with ML frameworks (e.g. TensorFlow PyTorch Scikit-learn).
Hands-on experience with Docker Kubernetes and infrastructure-as-code tools.
Familiarity with MLFlow model registries and monitoring tools.
Excellent problem-solving communication and collaboration skills.

Preferred Skills

Azure certifications (e.g. Azure AI Engineer Associate Azure Solutions Architect).
Experience with GenAI RAG (Retrieval-Augmented Generation) or LangChain-based architectures.
Exposure to hybrid onshore-offshore delivery models.
Knowledge of data privacy compliance and responsible AI practices.
Experience with automated testing and validation of ML models.

Key Responsibilities

Design and implement CI/CD pipelines for ML model training testing and deployment using Azure DevOps and Azure ML.
Manage the end-to-end lifecycle of ML models including versioning monitoring and retraining.
Deploy and manage models using Azure ML endpoints AKS and containerized environments (Docker/Kubernetes).
Collaborate with data scientists data engineers and architects to productionize ML solutions.
Ensure compliance with enterprise standards for security governance and auditability.
Monitor model performance detect drift and implement automated retraining workflows.
Optimize infrastructure for cost scalability and performance.
Maintain documentation and knowledge repositories for MLOps best practices.

Requirements

Required Qualifications

Bachelors or masters degree in computer science Engineering or a related field.
8 years of experience in IT with at least 3 years in MLOps or ML engineering roles.
Proficiency in Azure ML Azure DevOps and cloud-native services.
Strong programming skills in Python and experience with ML frameworks (e.g. TensorFlow PyTorch Scikit-learn).
Hands-on experience with Docker Kubernetes and infrastructure-as-code tools.
Familiarity with MLFlow model registries and monitoring tools.
Excellent problem-solving communication and collaboration skills.

Preferred Skills

Azure certifications (e.g. Azure AI Engineer Associate Azure Solutions Architect).
Experience with GenAI RAG (Retrieval-Augmented Generation) or LangChain-based architectures.
Exposure to hybrid onshore-offshore delivery models.
Knowledge of data privacy compliance and responsible AI practices.
Experience with automated testing and validation of ML models.