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# AI Engineer

- **Company:** Achmea B.V.
- **Location:** Leusden, Netherlands
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Code Review, Continuous Integration, Software Design Patterns, Monitoring of Systems, Python (Programming Language), Machine Learning, Tensorflow, SQL Databases, Data Processing, Feature Engineering, Pytorch, Delivery Pipeline, Deep Learning, Kubernetes, Machine Learning Operations, Software Version Control, Docker
- **Published:** September 11, 2026
- **Apply:** https://www.werk.nl/nl/vacatures/71278943

## About the Role

Role summary: Design, build, and deploy AI/ML solutions that turn data into reliable, scalable products. Partner with cross-functional teams to deliver models, services, and pipelines that meet performance, quality, and compliance requirements. Core responsibilities - Develop and optimize machine learning models (training, evaluation, tuning, and validation). - Build production-grade inference services and integrate models into applications via APIs. - Create and maintain data pipelines for feature engineering, labeling, and model retraining. - Implement MLOps practices: CI/CD for ML, model versioning, monitoring, and drift detection. - Collaborate with product, engineering, and stakeholders to translate requirements into technical solutions. - Ensure responsible AI practices, including privacy, security, bias testing, and documentation. Required skills - Programming: Python; strong software engineering fundamentals (testing, code review, design patterns). - ML frameworks: PyTorch
and/or TensorFlow; experience with classical ML and deep learning. - Data: SQL; data wrangling; feature engineering; experiment tracking. - Deployment: REST/gRPC APIs; Docker; cloud platforms (AWS/Azure/GCP) and scalable compute. - MLOps: MLflow/Kubeflow/SageMaker or similar; monitoring and observability. - Communication: clear documentation and ability to explain model behavior and trade-offs. Success measures - Models meet accuracy/latency/cost targets and are stable in production. - Automated pipelines reduce time-to-deploy and support repeatable ...



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