> Markdown version of [/jobs/ext/2062149-machine-learning-engineer-cloud-mlops-genai-relocation-provided](https://www.wearedevelopers.com/jobs/ext/2062149-machine-learning-engineer-cloud-mlops-genai-relocation-provided). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Machine Learning Engineer - Cloud | MLOps | GenAI (Relocation provided)

- **Company:** Wypoon Technologies
- **Location:** Amsterdam, Netherlands
- **Experience:** Expert
- **Contract:** Temporary contract
- **Skills:** Agile Methodology, Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Cloud Computing, Continuous Integration, Distributed Systems, Python (Programming Language), Machine Learning, Tensorflow, Azure Machine Learning, Software Engineering, Unstructured Data, Cloud Platform System, Feature Engineering, Pytorch, Large Language Models, Prompt Engineering, Generative AI, Data Lakes, Scikit Learn, Kubernetes, Information Technology, HuggingFace, Xgboost, Performance Monitor, Machine Learning Operations, Api Design, GPT, Software Version Control, Data Pipelines, Docker
- **Published:** August 15, 2026
- **Apply:** https://www.adzuna.nl/details/5843242420

## About the Role

* At least 5 years of professional experience.
 * Advanced proficiency in English (minimum B2 level in speaking, writing, listening, and reading).
 * A bachelor's degree in Computer Science, Software Engineering, or a related field.
 * Strong proficiency in Python, including libraries such as scikit-learn, TensorFlow, PyTorch, or XGBoost
 * Experience building and deploying ML models in at least one major cloud platform: Azure, AWS, or GCP
 * Familiarity with ML pipeline orchestration and CI/CD practices
 * Experience with Generative AI use cases, such as working with LLMs, embedding models, prompt engineering, or custom GPT integrations
 * Familiarity with HuggingFace Transformers, LangChain, or RAG architectures
 * Exposure to Responsible AI, explainability, or ethical ML practices
 * Background in MLOps tooling: MLflow, DVC, Tecton, Feast
 * Experience with data labeling tools or ML observability platforms
 * Solid understanding of software engineering principles and cloud infrastructure
 * Experience working with APIs, data lakes, and distributed systems is a plus
 * Comfortable in Agile environments and cross-functional teams

## Description

Are you a Machine Learning Engineer with a passion for building real-world, production-grade AI solutions in the cloud?
At Wypoon Technologies, we're expanding our network of skilled ML Engineers to support leading clients across the Netherlands. From predictive analytics to real-time recommendations and Generative AI use cases,you'll work on meaningful projects that scale.
Whether your focus lies in model development, MLOps, or cloud-native architecture, we're looking for engineers who can bring machine learning to life in enterprise environments., * Designing and implementing machine learning pipelines in cloud environments (Azure, AWS, or GCP)

 * Developing and deploying models for classification, regression, time series, recommendation, or NLP use cases
 * Working with structured and unstructured data, and applying feature engineering, model tuning, and evaluation techniques
 * Packaging and deploying models using containerization (e.g., Docker, Kubernetes)
 * Automating and monitoring ML workflows using MLflow, Airflow, or cloud-native tools (SageMaker, Vertex AI, Azure ML)
 * Collaborating with data scientists, engineers, and product teams to translate business problems into ML solutions
 * Contributing to MLOps practices: model versioning, CI/CD for ML, performance monitoring, and rollback strategies


## Related Videos

- [TikTok's Privacy Innovation](https://www.wearedevelopers.com/videos/1036-tiktok-s-privacy-innovation)
- [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered)
- [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension)
- [MLOps - What’s the deal behind it?](https://www.wearedevelopers.com/videos/392-mlops-what-s-the-deal-behind-it)
- [Explainable machine learning explained](https://www.wearedevelopers.com/videos/589-explainable-machine-learning-explained)
- [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker)

## Related Articles

- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud)