We are seeking a highly specialized Senior DevOps/Cloud Engineer with a deep focus on MLOps and Data Engineering to lead platform engineering and deployment automation on Azure. This role is critical for the end-to-end lifecycle management of AI/ML models, from training and validation to production-grade deployment and monitoring. You will be responsible for building secure, cost-efficient CI/CD pipelines and scalable data architectures to support advanced AI delivery.
...
Duration: 8-month contract
Location: Fully Remote
Assignment Type: 100% Remote
Rate: $567.28-$602.64/hr
Advantages
Fully Remote: Work from anywhere with a 100% remote assignment.
Cutting-Edge Stack: Hands-on leadership with Generative AI pipelines, Azure AI Foundry, and LLM orchestration.
Platform Leadership: Take a lead role in defining platform engineering standards and MLOps excellence for a high-performance team.
Stable Engagement: 8-month initial term focused on production-grade AI delivery and infrastructure modernization.
Responsibilities
DevOps & MLOps (Primary Focus)
Pipeline Engineering: Design and implement robust CI/CD pipelines using Azure DevOps, Git, and YAML across Dev, QA, and Production environments.
AI/ML Deployment: Lead the deployment of AI/ML models into production using automated pipelines; manage versioning and both batch and real-time inference.
Model Lifecycle: Implement full MLOps lifecycle management, including training, validation, monitoring for model drift, and retraining strategies.
Infrastructure as Code (IaC): Automate infrastructure provisioning using ARM, Bicep, or Terraform; manage Databricks clusters, scaling, and performance tuning.
Inference Endpoints: Develop scalable API-based model serving and integrate AI services such as Azure AI Search and Azure AI Foundry.
Data Engineering
Architecting Pipelines: Design and build complex data pipelines using Azure Data Factory (ADF), Databricks, and Azure Data Lake Storage (ADLS).
Medallion Architecture: Support and implement data processing layers (Bronze, Silver, Gold) and optimize ETL/ELT processes for large-scale data sets.
Database Management: Develop and optimize data models in Azure SQL, SQL Server, and Oracle.
Security & Cost Governance
Secure Architecture: Implement cloud security using RBAC, Managed Identities, and Azure Key Vault; secure ML endpoints via private endpoints and network controls.
Cost Optimization: Monitor cloud spend across Databricks and compute resources; implement auto-scaling and cluster right-sizing strategies.
Qualifications
Core Experience: 10+ years of IT experience with at least 5+ years specifically in DevOps, Data Engineering, or MLOps.
Azure Mastery: Expert-level knowledge of the Azure ecosystem, including ADF, Databricks, ADLS, and Azure SQL.
Automation Skills: 10+ years of experience designing CI/CD pipelines and working with YAML-based deployments.
ML Delivery: Proven track record of leading the deployment of AI/ML models into production and managing model lifecycle monitoring.
Data Proficiency: 10+ years of experience building data pipelines and working with relational databases like Oracle and SQL Server.
API Integration: Strong experience in building scalable REST API layers for model inference and data access.
Summary
If you are a technical leader with a passion for bridging the gap between Data Science and Production Engineering on the Azure platform, we encourage you to apply today!
Randstad Canada is committed to fostering a workforce reflective of all peoples of Canada. As a result, we are committed to developing and implementing strategies to increase the equity, diversity and inclusion within the workplace by examining our internal policies, practices, and systems throughout the entire lifecycle of our workforce, including its recruitment, retention and advancement for all employees. In addition to our deep commitment to respecting human rights, we are dedicated to positive actions to affect change to ensure everyone has full participation in the workforce free from any barriers, systemic or otherwise, especially equity-seeking groups who are usually underrepresented in Canada's workforce, including those who identify as women or non-binary/gender non-conforming; Indigenous or Aboriginal Peoples; persons with disabilities (visible or invisible) and; members of visible minorities, racialized groups and the LGBTQ2+ community.
Randstad Canada is committed to creating and maintaining an inclusive and accessible workplace for all its candidates and employees by supporting their accessibility and accommodation needs throughout the employment lifecycle. We ask that all job applications please identify any accommodation requirements by sending an email to accessibility@randstad.ca to ensure their ability to fully participate in the interview process.
This posting is for existing and upcoming vacancies.
show more
We are seeking a highly specialized Senior DevOps/Cloud Engineer with a deep focus on MLOps and Data Engineering to lead platform engineering and deployment automation on Azure. This role is critical for the end-to-end lifecycle management of AI/ML models, from training and validation to production-grade deployment and monitoring. You will be responsible for building secure, cost-efficient CI/CD pipelines and scalable data architectures to support advanced AI delivery.
Duration: 8-month contract
Location: Fully Remote
Assignment Type: 100% Remote
Rate: $567.28-$602.64/hr
Advantages
Fully Remote: Work from anywhere with a 100% remote assignment.
Cutting-Edge Stack: Hands-on leadership with Generative AI pipelines, Azure AI Foundry, and LLM orchestration.
Platform Leadership: Take a lead role in defining platform engineering standards and MLOps excellence for a high-performance team.
Stable Engagement: 8-month initial term focused on production-grade AI delivery and infrastructure modernization.
Responsibilities
DevOps & MLOps (Primary Focus)
Pipeline Engineering: Design and implement robust CI/CD pipelines using Azure DevOps, Git, and YAML across Dev, QA, and Production environments.
...
AI/ML Deployment: Lead the deployment of AI/ML models into production using automated pipelines; manage versioning and both batch and real-time inference.
Model Lifecycle: Implement full MLOps lifecycle management, including training, validation, monitoring for model drift, and retraining strategies.
Infrastructure as Code (IaC): Automate infrastructure provisioning using ARM, Bicep, or Terraform; manage Databricks clusters, scaling, and performance tuning.
Inference Endpoints: Develop scalable API-based model serving and integrate AI services such as Azure AI Search and Azure AI Foundry.
Data Engineering
Architecting Pipelines: Design and build complex data pipelines using Azure Data Factory (ADF), Databricks, and Azure Data Lake Storage (ADLS).
Medallion Architecture: Support and implement data processing layers (Bronze, Silver, Gold) and optimize ETL/ELT processes for large-scale data sets.
Database Management: Develop and optimize data models in Azure SQL, SQL Server, and Oracle.
Security & Cost Governance
Secure Architecture: Implement cloud security using RBAC, Managed Identities, and Azure Key Vault; secure ML endpoints via private endpoints and network controls.
Cost Optimization: Monitor cloud spend across Databricks and compute resources; implement auto-scaling and cluster right-sizing strategies.
Qualifications
Core Experience: 10+ years of IT experience with at least 5+ years specifically in DevOps, Data Engineering, or MLOps.
Azure Mastery: Expert-level knowledge of the Azure ecosystem, including ADF, Databricks, ADLS, and Azure SQL.
Automation Skills: 10+ years of experience designing CI/CD pipelines and working with YAML-based deployments.
ML Delivery: Proven track record of leading the deployment of AI/ML models into production and managing model lifecycle monitoring.
Data Proficiency: 10+ years of experience building data pipelines and working with relational databases like Oracle and SQL Server.
API Integration: Strong experience in building scalable REST API layers for model inference and data access.
Summary
If you are a technical leader with a passion for bridging the gap between Data Science and Production Engineering on the Azure platform, we encourage you to apply today!
Randstad Canada is committed to fostering a workforce reflective of all peoples of Canada. As a result, we are committed to developing and implementing strategies to increase the equity, diversity and inclusion within the workplace by examining our internal policies, practices, and systems throughout the entire lifecycle of our workforce, including its recruitment, retention and advancement for all employees. In addition to our deep commitment to respecting human rights, we are dedicated to positive actions to affect change to ensure everyone has full participation in the workforce free from any barriers, systemic or otherwise, especially equity-seeking groups who are usually underrepresented in Canada's workforce, including those who identify as women or non-binary/gender non-conforming; Indigenous or Aboriginal Peoples; persons with disabilities (visible or invisible) and; members of visible minorities, racialized groups and the LGBTQ2+ community.
Randstad Canada is committed to creating and maintaining an inclusive and accessible workplace for all its candidates and employees by supporting their accessibility and accommodation needs throughout the employment lifecycle. We ask that all job applications please identify any accommodation requirements by sending an email to accessibility@randstad.ca to ensure their ability to fully participate in the interview process.
This posting is for existing and upcoming vacancies.
show more
