MLOps EngineerYearly B2B contract (Freelance)Location Belgium-Haren (once a week or 3 days per month onsite)
Position SummaryWe 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 ResponsibilitiesInfrastructure & AutomationCollaborate 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 ownershipWork alongside DevOps and platform engineers to build reusable Infrastructure as Code (IaC) templates for ML environments, covering compute resources, storage, networking, and securityPartner with team members to ensure infrastructure is reproducible, version-controlled, and optimized for scalability across multiple domain-oriented data productsContribute to team efforts in establishing infrastructure standards and best practices for ML workloadsProvision and manage Azure ML workspaces, compute clusters, and related resources alongside Databricks infrastructureML Lifecycle ManagementDevelop automated end-to-end ML pipelines covering training, validation, deployment, and monitoring within a federated data architectureImplement ML workflows using both Azure ML and Databricks, selecting the appropriate platform based on use case requirementsImplement experiment tracking, model versioning, and artifact management using MLflow integrated with both Azure ML and Databricks environmentsLeverage Azure ML's model registry and Databricks MLflow Model Registry for unified model governance across platformsManage model promotion workflows across development, staging, and production environmentsDesign and implement feature store solutions for centralized feature engineering, versioning, and serving across ML workloadsEnable feature reusability and discoverability to support consistent model development across domain teamsData Mesh & Product ThinkingBuild MLOps functionalities within a development data platform following data mesh architecture principlesApply data-as-a-product mindset to ML models and features, ensuring they meet quality, discoverability, and usability standardsEstablish domain-agnostic MLOps capabilities that can be consumed by autonomous domain teamsImplement self-serve ML infrastructure enabling domain teams to independently develop, deploy, and manage modelsDefine and enforce data product standards including SLAs, data contracts, and quality metrics for ML features and modelsPlatform EngineeringConfigure and optimize both Azure ML compute instances and Azure Databricks clusters for performance and cost efficiency across federated domainsIntegrate Azure ML pipelines and Databricks workflows with CI/CD systems to enable seamless, automated model deploymentsEstablish interoperability between Azure ML and Databricks ecosystems, enabling data scientists to leverage strengths of both platformsEstablish best practices for platform usage and ML workflow orchestration in a decentralized environmentBuild feature store infrastructure (Azure ML Feature Store, Databricks Feature Store) that supports cross-domain feature sharing while maintaining domain autonomyMonitoring & OperationsBuild comprehensive monitoring systems to track model performance, data drift, feature quality, and infrastructure healthImplement monitoring solutions that span both Azure ML and Databricks deployments, providing unified observabilityDesign automated alerting and incident response processes for pipeline failures and degradationMaintain operational visibility across the full ML stack using observability toolsImplement governance and observability frameworks that provide transparency across domain-owned ML products
Required QualificationsCloud & 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 initiativesML 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 servingData 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 governanceDevelopment & 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 automationDeployment & 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