Pay: Salary not listed in the saved posting
Work setup: Unconfirmed
Employment: Unconfirmed

What you’ll work on

Design, build, and deploy end-to-end machine learning models using proprietary datasets, primarily within Azure Machine Learning
Own the full ML lifecycle, including data preparation, feature engineering, model training, evaluation, and validation
Collaborate with Data Scientists to identify and prioritize high-impact ML opportunities

From the employer’s posting

Key Responsibilities Design, build, and deploy end-to-end machine learning models using proprietary datasets, primarily within Azure Machine Learning Own the full ML lifecycle, including data preparation, feature engineering, model training, evaluation, and validation

Design, build, and deploy end-to-end machine learning models using proprietary datasets, primarily within Azure Machine Learning Own the full ML lifecycle, including data preparation, feature engineering, model training, evaluation, and validation Collaborate with Data Scientists to identify and prioritize high-impact ML opportunities

Own the full ML lifecycle, including data preparation, feature engineering, model training, evaluation, and validation Collaborate with Data Scientists to identify and prioritize high-impact ML opportunities Work with Principal AI Engineers to ensure models align with system architecture and engineering best practices across the Azure ecosystem

What you’ll bring

All qualifications

Core experience

Strong experience in machine learning engineering, with production deployment experience on Microsoft Azure
Experience with Azure OpenAI Service, Azure AI Studio, or Azure Cognitive Services
Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn)
Familiarity with containerization and orchestration tools (Docker, Azure Kubernetes Service)
Hands-on experience with Azure Machine Learning (AML Studio, AML SDK/CLI v2, endpoints, pipelines, model registry)
Experience with real-time or low-latency ML systems on Azure (Event Hubs, Stream Analytics)

Qualification wording

Strong experience in machine learning engineering, with production deployment experience on Microsoft Azure

Experience with Azure OpenAI Service, Azure AI Studio, or Azure Cognitive Services

Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn)

Familiarity with containerization and orchestration tools (Docker, Azure Kubernetes Service)

Hands-on experience with Azure Machine Learning (AML Studio, AML SDK/CLI v2, endpoints, pipelines, model registry)

Experience with real-time or low-latency ML systems on Azure (Event Hubs, Stream Analytics)

Tools in this posting

Python
Azure
Databricks
Docker
Kubernetes
PyTorch
TensorFlow
SQL
C#
Power BI
scikit-learn

Source

- Strong experience in machine learning engineering, with production deployment experience on Microsoft Azure - Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn) - Hands-on experience with Azure Machine Learning (AML Studio, AML SDK/CLI v2, endpoints, pipelines, model registry)

Blanc Labs is a premier partner for global enterprises, leading the way in digitization, automation, and the development of next-generation digital products and services. Our expertise in digital transformation powers businesses to accelerate service delivery, drive customer engagement, and foster growth. We are looking for a Senior Machine Learning Engineer to lead the end-to-end development and lifecycle management of machine learning models powered by proprietary data, within a Microsoft-centric technology environment. This is a highly impactful role at the core of our AI ecosystem, responsible for building scalable, production-grade ML systems on Azure that directly power intelligent product features. Key Responsibilities

Key Responsibilities - Design, build, and deploy end-to-end machine learning models using proprietary datasets, primarily within Azure Machine Learning - Own the full ML lifecycle, including data preparation, feature engineering, model training, evaluation, and validation

- Collaborate with Data Scientists to identify and prioritize high-impact ML opportunities - Work with Principal AI Engineers to ensure models align with system architecture and engineering best practices across the Azure ecosystem - Package and expose trained models as scalable APIs (Azure Functions, Azure App Service, or AKS) for consumption by AI Engineers and downstream systems

- Work with Principal AI Engineers to ensure models align with system architecture and engineering best practices across the Azure ecosystem - Package and expose trained models as scalable APIs (Azure Functions, Azure App Service, or AKS) for consumption by AI Engineers and downstream systems - Build and maintain model pipelines using Azure ML Pipelines / Azure DevOps, including versioning, retraining, and continuous improvement workflows

- Package and expose trained models as scalable APIs (Azure Functions, Azure App Service, or AKS) for consumption by AI Engineers and downstream systems - Build and maintain model pipelines using Azure ML Pipelines / Azure DevOps, including versioning, retraining, and continuous improvement workflows - Monitor model performance in production using Azure Monitor and Application Insights, ensuring accuracy, reliability, and relevance over time

- Build and maintain model pipelines using Azure ML Pipelines / Azure DevOps, including versioning, retraining, and continuous improvement workflows - Monitor model performance in production using Azure Monitor and Application Insights, ensuring accuracy, reliability, and relevance over time - Troubleshoot model drift, data quality issues, and performance bottlenecks

- Troubleshoot model drift, data quality issues, and performance bottlenecks - Partner with data engineering teams working in Azure Synapse, Azure Data Factory, and Azure Databricks to ensure clean, reliable data pipelines feeding ML workflows - Support integration of models with Microsoft-native AI services, including Azure OpenAI Service and Azure AI Studio, where applicable

- Partner with data engineering teams working in Azure Synapse, Azure Data Factory, and Azure Databricks to ensure clean, reliable data pipelines feeding ML workflows - Support integration of models with Microsoft-native AI services, including Azure OpenAI Service and Azure AI Studio, where applicable Qualifications

Qualifications - Strong experience in machine learning engineering, with production deployment experience on Microsoft Azure - Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn)

- Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn) - Hands-on experience with Azure Machine Learning (AML Studio, AML SDK/CLI v2, endpoints, pipelines, model registry) - Experience with data preprocessing, feature engineering, and model evaluation techniques, using Azure Databricks and/or Azure Synapse Analytics

- Hands-on experience with Azure Machine Learning (AML Studio, AML SDK/CLI v2, endpoints, pipelines, model registry) - Experience with data preprocessing, feature engineering, and model evaluation techniques, using Azure Databricks and/or Azure Synapse Analytics - Hands-on experience deploying ML models as APIs or microservices using Azure-native compute (Azure Functions, App Service, or Azure Kubernetes Service)

- Experience with data preprocessing, feature engineering, and model evaluation techniques, using Azure Databricks and/or Azure Synapse Analytics - Hands-on experience deploying ML models as APIs or microservices using Azure-native compute (Azure Functions, App Service, or Azure Kubernetes Service) - Familiarity with MLOps practices on Azure, including model versioning (AML Model Registry), CI/CD via Azure DevOps, monitoring, and retraining pipelines

- Hands-on experience deploying ML models as APIs or microservices using Azure-native compute (Azure Functions, App Service, or Azure Kubernetes Service) - Familiarity with MLOps practices on Azure, including model versioning (AML Model Registry), CI/CD via Azure DevOps, monitoring, and retraining pipelines - Experience working with large-scale or proprietary datasets stored in Azure Data Lake Storage / Azure SQL

- Familiarity with MLOps practices on Azure, including model versioning (AML Model Registry), CI/CD via Azure DevOps, monitoring, and retraining pipelines - Experience working with large-scale or proprietary datasets stored in Azure Data Lake Storage / Azure SQL - Strong software engineering fundamentals (testing, scalability, performance optimization) with familiarity in .NET or C# environments an asset given the broader Microsoft stack

Nice-to-Have - Experience with Azure OpenAI Service, Azure AI Studio, or Azure Cognitive Services - Familiarity with containerization and orchestration tools (Docker, Azure Kubernetes Service)

- Experience with Azure OpenAI Service, Azure AI Studio, or Azure Cognitive Services - Familiarity with containerization and orchestration tools (Docker, Azure Kubernetes Service) - Experience with real-time or low-latency ML systems on Azure (Event Hubs, Stream Analytics)

- Familiarity with containerization and orchestration tools (Docker, Azure Kubernetes Service) - Experience with real-time or low-latency ML systems on Azure (Event Hubs, Stream Analytics) - Exposure to LLMs or hybrid AI/ML systems, particularly via Azure OpenAI or Microsoft Copilot ecosystem

- Experience with real-time or low-latency ML systems on Azure (Event Hubs, Stream Analytics) - Exposure to LLMs or hybrid AI/ML systems, particularly via Azure OpenAI or Microsoft Copilot ecosystem - Experience building data pipelines with Azure Data Factory, Synapse Pipelines, or Databricks workflows

- Exposure to LLMs or hybrid AI/ML systems, particularly via Azure OpenAI or Microsoft Copilot ecosystem - Experience building data pipelines with Azure Data Factory, Synapse Pipelines, or Databricks workflows - Microsoft certifications (e.g., Azure AI Engineer Associate, Azure Data Scientist Associate, Azure Solutions Architect) considered a plus

- Experience building data pipelines with Azure Data Factory, Synapse Pipelines, or Databricks workflows - Microsoft certifications (e.g., Azure AI Engineer Associate, Azure Data Scientist Associate, Azure Solutions Architect) considered a plus - Exposure to Power Platform (Power BI, Power Automate) for surfacing ML outputs to business stakeholders

- Experience working with large-scale or proprietary datasets stored in Azure Data Lake Storage / Azure SQL - Strong software engineering fundamentals (testing, scalability, performance optimization) with familiarity in .NET or C# environments an asset given the broader Microsoft stack - Ability to collaborate effectively with cross-functional teams in a Microsoft-centric enterprise environment

- Microsoft certifications (e.g., Azure AI Engineer Associate, Azure Data Scientist Associate, Azure Solutions Architect) considered a plus - Exposure to Power Platform (Power BI, Power Automate) for surfacing ML outputs to business stakeholders Blanc Labs is an equal opportunity employer and is committed to employing in accordance with the Ontario Human Rights Code and the Accessibility for Ontarians with Disabilities Act. Accommodations within reason due to a disability or medical need are available on request for candidates taking part in the recruitment process.

Job description

View original posting ↗

Blanc Labs is a premier partner for global enterprises, leading the way in digitization, automation, and the development of next-generation digital products and services. Our expertise in digital transformation powers businesses to accelerate service delivery, drive customer engagement, and foster growth.

We are looking for a Senior Machine Learning Engineer to lead the end-to-end development and lifecycle management of machine learning models powered by proprietary data, within a Microsoft-centric technology environment. This is a highly impactful role at the core of our AI ecosystem, responsible for building scalable, production-grade ML systems on Azure that directly power intelligent product features.

Key Responsibilities

Design, build, and deploy end-to-end machine learning models using proprietary datasets, primarily within Azure Machine Learning
Own the full ML lifecycle, including data preparation, feature engineering, model training, evaluation, and validation
Collaborate with Data Scientists to identify and prioritize high-impact ML opportunities
Work with Principal AI Engineers to ensure models align with system architecture and engineering best practices across the Azure ecosystem
Package and expose trained models as scalable APIs (Azure Functions, Azure App Service, or AKS) for consumption by AI Engineers and downstream systems
Build and maintain model pipelines using Azure ML Pipelines / Azure DevOps, including versioning, retraining, and continuous improvement workflows
Monitor model performance in production using Azure Monitor and Application Insights, ensuring accuracy, reliability, and relevance over time
Troubleshoot model drift, data quality issues, and performance bottlenecks
Partner with data engineering teams working in Azure Synapse, Azure Data Factory, and Azure Databricks to ensure clean, reliable data pipelines feeding ML workflows
Support integration of models with Microsoft-native AI services, including Azure OpenAI Service and Azure AI Studio, where applicable

Qualifications

Strong experience in machine learning engineering, with production deployment experience on Microsoft Azure
Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn)
Hands-on experience with Azure Machine Learning (AML Studio, AML SDK/CLI v2, endpoints, pipelines, model registry)
Experience with data preprocessing, feature engineering, and model evaluation techniques, using Azure Databricks and/or Azure Synapse Analytics
Hands-on experience deploying ML models as APIs or microservices using Azure-native compute (Azure Functions, App Service, or Azure Kubernetes Service)
Familiarity with MLOps practices on Azure, including model versioning (AML Model Registry), CI/CD via Azure DevOps, monitoring, and retraining pipelines
Experience working with large-scale or proprietary datasets stored in Azure Data Lake Storage / Azure SQL
Strong software engineering fundamentals (testing, scalability, performance optimization) with familiarity in .NET or C# environments an asset given the broader Microsoft stack
Ability to collaborate effectively with cross-functional teams in a Microsoft-centric enterprise environment

Nice-to-Have

Experience with Azure OpenAI Service, Azure AI Studio, or Azure Cognitive Services
Familiarity with containerization and orchestration tools (Docker, Azure Kubernetes Service)
Experience with real-time or low-latency ML systems on Azure (Event Hubs, Stream Analytics)
Exposure to LLMs or hybrid AI/ML systems, particularly via Azure OpenAI or Microsoft Copilot ecosystem
Experience building data pipelines with Azure Data Factory, Synapse Pipelines, or Databricks workflows
Microsoft certifications (e.g., Azure AI Engineer Associate, Azure Data Scientist Associate, Azure Solutions Architect) considered a plus
Exposure to Power Platform (Power BI, Power Automate) for surfacing ML outputs to business stakeholders

Blanc Labs is an equal opportunity employer and is committed to employing in accordance with the Ontario Human Rights Code and the Accessibility for Ontarians with Disabilities Act. Accommodations within reason due to a disability or medical need are available on request for candidates taking part in the recruitment process.

Blanc Labs is enabling a digital future. Headquartered in Toronto, we partner with clients in North & South America to digitize and automate their operations and build their next generation of digital products and services. We empower clients to enhance their digital offerings and bring creative solutions to the market faster. Learn more at www.blanclabs.com.

Your next step

Have your CV and examples of relevant work ready.
Check the listed location, eligibility and core experience before starting.
Ask the employer about the salary range before committing time to the process.

Complete your application on blanclabs.bamboohr.com. The employer’s form will show what is required.

Already applied? Track this application

Source & posting history

View original posting ↗

Source notes

Source excerpts

Selected passages from the saved posting. Check the full description for conditions and exceptions.

Pay: No pay amount identified in the saved description.
Location & working pattern: Location not supplied.
- Experience with real-time or low-latency ML systems on Azure (Event Hubs, Stream Analytics) - Exposure to LLMs or hybrid AI/ML systems, particularly via Azure OpenAI or Microsoft Copilot ecosystem - Experience building data pipelines with Azure Data Factory, Synapse Pipelines, or Databricks workflows
Work authorization: No clear work-authorization passage found. Eligibility is unconfirmed.

Open source at blanclabs.bamboohr.com ↗Read the full description ↓

Status in our records: Active
First seen by us: Jun 4, 2026
Recorded sightings: 49
Last seen by us: Oct 9, 2026

These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.

Report an error

See how this role fits your experience

Add your resume to compare the role’s scope, tools and requirements with your experience.

Upload resume Add LinkedIn