Machine Learning Engineer
Solliciteren
Plaats
BrusselsBelgië
België
Contract
Vooltijds, Onbepaalde duurDatum gepubliceerd
2026-03-04Online to:
2026-05-03EUR 15.00 HOUR
Machine Learning Engineer
MLOps Engineer
Yearly B2B Contract (Freelance)
Location Brussels-Haren (once a week onsite)
Position Summary We are looking for an experienced MLOps Engineer to architect and operationalize scalable machine learning infrastructure on Azure within a decentralized data platform environment. You will own the complete ML lifecycle—from development through production—leveraging a hybrid Azure ML and Databricks ecosystem, using infrastructure-as-code practices and MLflow to deliver automated, reliable, and cost-effective ML operations. This role requires building MLOps capabilities that align with data mesh principles, treating data and models as products with clear ownership and domain-driven architecture.
Core Responsibilities Infrastructure & Automation Collaborate with cross-functional infrastructure and platform teams to design and deploy production-grade MLOps infrastructure on Azure using Terraform, adhering to data mesh principles of decentralized domain ownership Work alongside DevOps and platform engineers to build reusable Infrastructure as Code (IaC) templates for ML environments, covering compute resources, storage, networking, and security Partner with team members to ensure infrastructure is reproducible, version-controlled, and optimized for scalability across multiple domain-oriented data products Contribute to team efforts in establishing infrastructure standards and best practices for ML workloads Provision and manage Azure ML workspaces, compute clusters, and related resources alongside Databricks infrastructure ML Lifecycle Management Develop automated end-to-end ML pipelines covering training, validation, deployment, and monitoring within a federated data architecture Implement ML workflows using both Azure ML and Databricks, selecting the appropriate platform based on use case requirements Implement experiment tracking, model versioning, and artifact management using MLflow integrated with both Azure ML and Databricks environments Leverage Azure ML's model registry and Databricks MLflow Model Registry for unified model governance across platforms Manage model promotion workflows across development, staging, and production environments Design and implement feature store solutions for centralized feature engineering, versioning, and serving across ML workloads Enable feature reusability and discoverability to support consistent model development across domain teams Data Mesh & Product Thinking Build MLOps functionalities within a development data platform following data mesh architecture principles Apply data-as-a-product mindset to ML models and features, ensuring they meet quality, discoverability, and usability standards Establish domain-agnostic MLOps capabilities that can be consumed by autonomous domain teams Implement self-serve ML infrastructure enabling domain teams to independently develop, deploy, and manage models Define and enforce data product standards including SLAs, data contracts, and quality metrics for ML features and models Platform Engineering Configure and optimize both Azure ML compute instances and Azure Databricks clusters for performance and cost efficiency across federated domains Integrate Azure ML pipelines and Databricks workflows with CI/CD systems to enable seamless, automated model deployments Establish interoperability between Azure ML and Databricks ecosystems, enabling data scientists to leverage strengths of both platforms Establish best practices for platform usage and ML workflow orchestration in a decentralized environment Build feature store infrastructure (Azure ML Feature Store, Databricks Feature Store) that supports cross-domain feature sharing while maintaining domain autonomy Monitoring & Operations Build comprehensive monitoring systems to track model performance, data drift, feature quality, and infrastructure health Implement monitoring solutions that span both Azure ML and Databricks deployments, providing unified observability Design automated alerting and incident response processes for pipeline failures and degradation Maintain operational visibility across the full ML stack using observability tools Implement governance and observability frameworks that provide transparency across domain-owned ML products
Required Qualifications Cloud & Infrastructure
- Hands-on expertise with Azure services including compute, storage, networking, and security tailored for ML workloads - Advanced proficiency in Terraform with proven experience managing complex, multi-environment infrastructure -
Demonstrated ability to collaborate effectively with infrastructure and DevOps teams on shared platform initiatives ML Platform & Tools
- Deep knowledge of Azure ML including workspace management, compute resources, pipeline orchestration, model deployment (managed endpoints, AKS), and MLOps capabilities - Deep knowledge of Azure Databricks, including cluster management, job orchestration, and Azure integrations - Experience integrating Azure ML and Databricks ecosystems to create unified ML workflows - Extensive experience with MLflow for experiment tracking, model registry, model serving, and production lifecycle management across both platforms - Proven experience designing and implementing feature stores (Azure ML Feature Store, Databricks Feature Store, or Feast) for online and offline feature serving Data Mesh & Platform Architecture
- Understanding of data mesh principles including domain ownership, data as a product, self-serve data infrastructure, and federated computational governance - Experience building platform capabilities that enable autonomous domain teams while maintaining organizational standards - Ability to design ML systems that support decentralized ownership with centralized governance Development & Automation
- Strong Python programming skills with familiarity in ML frameworks (scikit-learn, TensorFlow, PyTorch) and data processing libraries - Demonstrated ability to build CI/CD pipelines for ML systems using Azure DevOps, GitHub Actions, or similar platforms, including automated testing and deployment strategies - Experience with Azure ML SDK/CLI and Databricks APIs for workflow automation Deployment & Monitoring
- Solid understanding of containerization (Docker, Kubernetes) for ML model deployment and scaling - Experience with Azure ML model deployment options including managed endpoints, AKS, and Azure Container Instances - Experience with monitoring and observability platforms such as Azure Monitor, Application Insights, or equivalent tools for tracking model and infrastructure metrics - Experience implementing data quality monitoring and feature drift detection in production environments
Position Summary We are looking for an experienced MLOps Engineer to architect and operationalize scalable machine learning infrastructure on Azure within a decentralized data platform environment. You will own the complete ML lifecycle—from development through production—leveraging a hybrid Azure ML and Databricks ecosystem, using infrastructure-as-code practices and MLflow to deliver automated, reliable, and cost-effective ML operations. This role requires building MLOps capabilities that align with data mesh principles, treating data and models as products with clear ownership and domain-driven architecture.
Core Responsibilities Infrastructure & Automation Collaborate with cross-functional infrastructure and platform teams to design and deploy production-grade MLOps infrastructure on Azure using Terraform, adhering to data mesh principles of decentralized domain ownership Work alongside DevOps and platform engineers to build reusable Infrastructure as Code (IaC) templates for ML environments, covering compute resources, storage, networking, and security Partner with team members to ensure infrastructure is reproducible, version-controlled, and optimized for scalability across multiple domain-oriented data products Contribute to team efforts in establishing infrastructure standards and best practices for ML workloads Provision and manage Azure ML workspaces, compute clusters, and related resources alongside Databricks infrastructure ML Lifecycle Management Develop automated end-to-end ML pipelines covering training, validation, deployment, and monitoring within a federated data architecture Implement ML workflows using both Azure ML and Databricks, selecting the appropriate platform based on use case requirements Implement experiment tracking, model versioning, and artifact management using MLflow integrated with both Azure ML and Databricks environments Leverage Azure ML's model registry and Databricks MLflow Model Registry for unified model governance across platforms Manage model promotion workflows across development, staging, and production environments Design and implement feature store solutions for centralized feature engineering, versioning, and serving across ML workloads Enable feature reusability and discoverability to support consistent model development across domain teams Data Mesh & Product Thinking Build MLOps functionalities within a development data platform following data mesh architecture principles Apply data-as-a-product mindset to ML models and features, ensuring they meet quality, discoverability, and usability standards Establish domain-agnostic MLOps capabilities that can be consumed by autonomous domain teams Implement self-serve ML infrastructure enabling domain teams to independently develop, deploy, and manage models Define and enforce data product standards including SLAs, data contracts, and quality metrics for ML features and models Platform Engineering Configure and optimize both Azure ML compute instances and Azure Databricks clusters for performance and cost efficiency across federated domains Integrate Azure ML pipelines and Databricks workflows with CI/CD systems to enable seamless, automated model deployments Establish interoperability between Azure ML and Databricks ecosystems, enabling data scientists to leverage strengths of both platforms Establish best practices for platform usage and ML workflow orchestration in a decentralized environment Build feature store infrastructure (Azure ML Feature Store, Databricks Feature Store) that supports cross-domain feature sharing while maintaining domain autonomy Monitoring & Operations Build comprehensive monitoring systems to track model performance, data drift, feature quality, and infrastructure health Implement monitoring solutions that span both Azure ML and Databricks deployments, providing unified observability Design automated alerting and incident response processes for pipeline failures and degradation Maintain operational visibility across the full ML stack using observability tools Implement governance and observability frameworks that provide transparency across domain-owned ML products
Required Qualifications Cloud & Infrastructure
- Hands-on expertise with Azure services including compute, storage, networking, and security tailored for ML workloads - Advanced proficiency in Terraform with proven experience managing complex, multi-environment infrastructure -
Demonstrated ability to collaborate effectively with infrastructure and DevOps teams on shared platform initiatives ML Platform & Tools
- Deep knowledge of Azure ML including workspace management, compute resources, pipeline orchestration, model deployment (managed endpoints, AKS), and MLOps capabilities - Deep knowledge of Azure Databricks, including cluster management, job orchestration, and Azure integrations - Experience integrating Azure ML and Databricks ecosystems to create unified ML workflows - Extensive experience with MLflow for experiment tracking, model registry, model serving, and production lifecycle management across both platforms - Proven experience designing and implementing feature stores (Azure ML Feature Store, Databricks Feature Store, or Feast) for online and offline feature serving Data Mesh & Platform Architecture
- Understanding of data mesh principles including domain ownership, data as a product, self-serve data infrastructure, and federated computational governance - Experience building platform capabilities that enable autonomous domain teams while maintaining organizational standards - Ability to design ML systems that support decentralized ownership with centralized governance Development & Automation
- Strong Python programming skills with familiarity in ML frameworks (scikit-learn, TensorFlow, PyTorch) and data processing libraries - Demonstrated ability to build CI/CD pipelines for ML systems using Azure DevOps, GitHub Actions, or similar platforms, including automated testing and deployment strategies - Experience with Azure ML SDK/CLI and Databricks APIs for workflow automation Deployment & Monitoring
- Solid understanding of containerization (Docker, Kubernetes) for ML model deployment and scaling - Experience with Azure ML model deployment options including managed endpoints, AKS, and Azure Container Instances - Experience with monitoring and observability platforms such as Azure Monitor, Application Insights, or equivalent tools for tracking model and infrastructure metrics - Experience implementing data quality monitoring and feature drift detection in production environments
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