Machine Learning Engineer - Hybrid Remote, Atos

Report this listing

Solliciteren

Plaats

Laeken
België
België

Contract

Vooltijds, Onbepaalde duur

Datum gepubliceerd

2026-03-03

Online to:

2026-05-03
EUR 20.00 HOUR

Machine Learning Engineer - Hybrid Remote

Atos
België
9 uren geleden

MLOps Engineer Yearly B2B Contract (Freelance) We are looking for an experienced MLOps Engineer to architect and operationalize scalable machine learning infrastructure on Azure within a decentralized data platform environment. 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. 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 Develop automated end-to-end ML pipelines covering training, validation, deployment, and monitoring within a federated data architecture 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 Establish interoperability between Azure ML and Databricks ecosystems, enabling data scientists to leverage strengths of both platforms Build comprehensive monitoring systems to track model performance, data drift, feature quality, and infrastructure health 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

Solliciteren

Machine Learning Engineer - Hybrid Remote

Plaats

Contract

Datum gepubliceerd

Online to:

Machine Learning Engineer - Hybrid Remote

Meer banen van je zoekopdracht

Calculator Infra

Metaalbewerker | Mol

HR-Manager | Zwevezele | Vast contract

Ploegbaas productie | vaste nacht | Diepenbeek |

Conducteur de Travaux Gros Oeuvres

Bulldozer Chauffeur

financieel adviseur

Head of Growth (remote)

VIE (12 months) - QA/QC Assistant

Ouvrier de Production (H/F/X)

Professeur.e de cuisine familiale au DI (H/F/X)

Technisch Commercieel Medewerker

Mécaniciens Automobile M/F