Senior Machine Learning Engineer

Location not stated

Open 5 months, first seen by us 153 days ago

Pay: Pay not listed
Work setting: Not stated
Employment: Not stated

What you’ll work on

Design, build and deploy end-to-end ML models, mainly in Azure Machine Learning
Own the full ML lifecycle: data prep, feature engineering, training, evaluation
Package trained models as scalable APIs on Azure Functions, App Service or AKS
Build and maintain model pipelines with versioning and retraining workflows
Monitor production models with Azure Monitor and Application Insights for drift

From the employer’s posting

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

Package and expose trained models as scalable APIs (Azure Functions, Azure App Service, or AKS)

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

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)

Apply at Blanc Labs

Posting record

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Open 5 monthsWe first saw this posting 153 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.

Status in our records: Live
First seen by us: Jun 4, 2026153 days ago
Recorded sightings: 71
Last seen by us: Nov 4, 2026

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

How this job compares

Time since we first saw it

Day 153

Up longer than most. Similar jobs usually come down between day 6 and day 69.

From 4,339 similar postings that came down. Coming down is not always a hire.

Blanc Labs is hiring

5 open jobs

Takes jobs down at a usual pace

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

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

Where the posting names them

- 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

- 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)

- 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

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

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

- 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.

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About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025: $140,300
Expected change, 2025 to 2035: 21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

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.

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