We are seeking a highly technical, forward-thinking Senior Data Engineer / MLOps Platform Lead to pioneer the design, expansion, and optimization of our enterprise Machine Learning Operations (MLOps) ecosystem. In this role, you will serve as the definitive technical authority establishing global standards for infrastructure automation, pipeline orchestration, and system observability supporting the end-to-end machine learning model lifecycle.
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This position sits at the intersection of big data engineering, advanced cloud infrastructure architecture, and data science. You will simplify, standardize, and consolidate fragmented ML workflows across multiple cross-functional teams while ensuring the absolute reliability, security, and performance of our production cloud environments.
Location: Vancouver, BC (Hybrid – 4 days per week onsite)
Contract Duration: 6-month contract with high likelihood of extension
Advantages
Strategic Architectural Influence: Build and define the net-new global standard for machine learning infrastructure for a premium corporate brand.
Advanced Tech Spectrum: Work natively with cutting-edge data tech: Unity Catalog, serverless Azure frameworks, and modern IaC toolsets.
Elite Collaboration Hub: Form part of an energetic, values-driven onsite workspace in Vancouver that fosters innovation and entrepreneurial spirit.
Long-Term Program Depth: Secure an initial 6-month contract with highly probable rolling extensions as the global platform scales.
Responsibilities
1. MLOps Technical Leadership & Workflow Simplification
Define the enterprise standard architecture for MLOps, focusing on infrastructure scaling, automated continuous training (CT), and deployment observability.
Consolidate and simplify disparate machine learning workflows across varied global data science teams into a unified platform.
Build and scale robust ML/AI orchestration pipelines utilizing Databricks, Unity Catalog, and MLflow for model tracking, lineage tracking, and governance.
2. Cloud Infrastructure as Code (IaC) & Administration
Architect and manage secure enterprise cloud environments natively within Azure using Terraform for Infrastructure as Code (IaC).
Automate the provisioning of complex network configurations, cloud resources, IAM security privileges, and containerized configurations.
Monitor cloud environment footprint performance, guaranteeing high availability, structural reliability, and cost optimization.
3. Production Deployment & Continuous Delivery
Oversee and manage large-scale production deployments of batch and real-time machine learning models.
Standardize the continuous integration and continuous delivery (CI/CD) pipelines utilizing Azure DevOps, Jenkins, or GitLab.
Implement containerization and deployment orchestration frameworks across critical corporate data domains.
4. Observability, Incident Response & Platform Operations
Design and implement advanced telemetry, monitoring metrics, and proactive alerting frameworks for distributed cloud infrastructure and data apps.
Act as the technical lead during critical system outages or customer escalations, orchestrating rapid incident resolution workflows and bridging communication across internal and external global vendors.
Provide technical guidance, code review governance, and structured mentorship to intermediate and junior engineers.
Qualifications
Core MLOps Tenure: 4+ years of hands-on experience building, scaling, and maintaining production-grade MLOps pipelines using the Azure data ecosystem.
Databricks Suite Mastery: 3+ years of proven success building AI workflows specifically utilizing Databricks, Unity Catalog (for governance), and MLflow (for model tracking).
Azure Cloud Administration: 3+ years of documented experience in core Azure infrastructure management, networking boundaries, and secure enterprise provisioning.
Data Engineering Stack: Expert-level proficiency in SQL, Spark SQL, Python, and PySpark data manipulation scripts.
Infrastructure & Orchestration Tooling: Proficient with Terraform (IaC), Apache Airflow, Azure Data Factory, Azure Functions, Snowflake, and Fabric OneLake environments.
Crisis Management: Demonstrated capability in active crisis management, handling customer escalations, and troubleshooting distributed runtime system failures.
Education: Bachelor’s degree in Computer Science, Software Engineering, or an equivalent technical field.
Preferred Asset Qualifications (Nice-to-Haves)
Technical exposure to data architectures and machine learning tools across alternate cloud providers (AWS or GCP).
Prior experience delivering data platform engineering within the retail or digital e-commerce sector.
Active certifications such as Microsoft Certified: Azure Data Engineer Associate or Databricks Certified Machine Learning Professional.
Summary
If you are a Senior Data Engineer with a deep passion for scaling MLOps infrastructure and building automated pathways that transition machine learning prototypes into production-grade systems, we encourage you to apply online at www.randstad.ca. Only qualified candidates will be contacted for the next steps. We look forward to hearing from you!
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.
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We are seeking a highly technical, forward-thinking Senior Data Engineer / MLOps Platform Lead to pioneer the design, expansion, and optimization of our enterprise Machine Learning Operations (MLOps) ecosystem. In this role, you will serve as the definitive technical authority establishing global standards for infrastructure automation, pipeline orchestration, and system observability supporting the end-to-end machine learning model lifecycle.
This position sits at the intersection of big data engineering, advanced cloud infrastructure architecture, and data science. You will simplify, standardize, and consolidate fragmented ML workflows across multiple cross-functional teams while ensuring the absolute reliability, security, and performance of our production cloud environments.
Location: Vancouver, BC (Hybrid – 4 days per week onsite)
Contract Duration: 6-month contract with high likelihood of extension
Advantages
Strategic Architectural Influence: Build and define the net-new global standard for machine learning infrastructure for a premium corporate brand.
Advanced Tech Spectrum: Work natively with cutting-edge data tech: Unity Catalog, serverless Azure frameworks, and modern IaC toolsets.
...
Elite Collaboration Hub: Form part of an energetic, values-driven onsite workspace in Vancouver that fosters innovation and entrepreneurial spirit.
Long-Term Program Depth: Secure an initial 6-month contract with highly probable rolling extensions as the global platform scales.
Responsibilities
1. MLOps Technical Leadership & Workflow Simplification
Define the enterprise standard architecture for MLOps, focusing on infrastructure scaling, automated continuous training (CT), and deployment observability.
Consolidate and simplify disparate machine learning workflows across varied global data science teams into a unified platform.
Build and scale robust ML/AI orchestration pipelines utilizing Databricks, Unity Catalog, and MLflow for model tracking, lineage tracking, and governance.
2. Cloud Infrastructure as Code (IaC) & Administration
Architect and manage secure enterprise cloud environments natively within Azure using Terraform for Infrastructure as Code (IaC).
Automate the provisioning of complex network configurations, cloud resources, IAM security privileges, and containerized configurations.
Monitor cloud environment footprint performance, guaranteeing high availability, structural reliability, and cost optimization.
3. Production Deployment & Continuous Delivery
Oversee and manage large-scale production deployments of batch and real-time machine learning models.
Standardize the continuous integration and continuous delivery (CI/CD) pipelines utilizing Azure DevOps, Jenkins, or GitLab.
Implement containerization and deployment orchestration frameworks across critical corporate data domains.
4. Observability, Incident Response & Platform Operations
Design and implement advanced telemetry, monitoring metrics, and proactive alerting frameworks for distributed cloud infrastructure and data apps.
Act as the technical lead during critical system outages or customer escalations, orchestrating rapid incident resolution workflows and bridging communication across internal and external global vendors.
Provide technical guidance, code review governance, and structured mentorship to intermediate and junior engineers.
Qualifications
Core MLOps Tenure: 4+ years of hands-on experience building, scaling, and maintaining production-grade MLOps pipelines using the Azure data ecosystem.
Databricks Suite Mastery: 3+ years of proven success building AI workflows specifically utilizing Databricks, Unity Catalog (for governance), and MLflow (for model tracking).
Azure Cloud Administration: 3+ years of documented experience in core Azure infrastructure management, networking boundaries, and secure enterprise provisioning.
Data Engineering Stack: Expert-level proficiency in SQL, Spark SQL, Python, and PySpark data manipulation scripts.
Infrastructure & Orchestration Tooling: Proficient with Terraform (IaC), Apache Airflow, Azure Data Factory, Azure Functions, Snowflake, and Fabric OneLake environments.
Crisis Management: Demonstrated capability in active crisis management, handling customer escalations, and troubleshooting distributed runtime system failures.
Education: Bachelor’s degree in Computer Science, Software Engineering, or an equivalent technical field.
Preferred Asset Qualifications (Nice-to-Haves)
Technical exposure to data architectures and machine learning tools across alternate cloud providers (AWS or GCP).
Prior experience delivering data platform engineering within the retail or digital e-commerce sector.
Active certifications such as Microsoft Certified: Azure Data Engineer Associate or Databricks Certified Machine Learning Professional.
Summary
If you are a Senior Data Engineer with a deep passion for scaling MLOps infrastructure and building automated pathways that transition machine learning prototypes into production-grade systems, we encourage you to apply online at www.randstad.ca. Only qualified candidates will be contacted for the next steps. We look forward to hearing from you!
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.
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