BearingPoint

Senior MLOps Engineer

BearingPoint

contract

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Origin:  • 🇳🇱 Netherlands

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Job Level

Senior

Tech Stack

AWSAzureCloudPythonPyTorchScikit-LearnTensorflow

About the role

  • Design and deploy scalable MLOps frameworks that streamline transition from data science to production
  • Guide clients in operationalizing machine learning models for long-term impact and sustainable performance
  • Lead MLOps implementations: design and build end-to-end MLOps pipelines, establish CI/CD practices for ML models, and automate deployment processes
  • Ensure scalable and reliable Machine Learning systems in complex enterprise environments
  • Drive innovation and thought leadership in MLOps, model governance, responsible AI, and GenAI
  • Promote best practices and lead adoption of modern ML infrastructures across client organizations
  • Mentor and guide teams: manage initiatives, coach junior engineers, and facilitate knowledge sharing
  • Lead model governance discussions and develop innovative solutions to complex ML deployment challenges
  • Support client development: shape ML infrastructure strategies and architect scalable platforms to enable AI transformations

Requirements

  • A relevant and completed Master of Science degree
  • Certifications in cloud platforms (Azure and/or AWS)
  • 6–8 years of experience in DevOps, ML engineering, or software engineering with a focus on ML (preferably consulting)
  • Extensive hands-on experience with MLOps tools and platforms
  • Strong programming skills in Python
  • Experience with ML frameworks (TensorFlow, PyTorch, scikit-learn, XGBoost)
  • Deep expertise in containerization and orchestration technologies for ML workloads
  • Experience with cloud-native ML services and serverless computing for ML workloads
  • Experience with Infrastructure as Code (IaC)
  • Experience with stream processing and real-time ML inference architecture
  • Experience with model monitoring, drift detection, and ML observability tools
  • Understanding of ML security, testing strategies, model governance, and responsible AI practices
  • Knowledge of feature stores, model registries, data- and model-versioning, and experiment tracking systems
  • Good communication and presentation skills in both Dutch and English
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