Wypoon Technologies

Machine Learning Engineer – Cloud, MLOps, GenAI

Wypoon Technologies

full-time

Posted on:

Origin:  • 🇳🇱 Netherlands

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Job Level

Mid-LevelSenior

Tech Stack

AirflowAWSAzureCloudDistributed SystemsDockerGoogle Cloud PlatformKubernetesPythonPyTorchScikit-LearnTensorflow

About the role

  • Design and implement machine learning pipelines in cloud environments (Azure, AWS, or GCP).
  • Develop and deploy models for classification, regression, time series, recommendation, or NLP use cases.
  • Work with structured and unstructured data; apply feature engineering, model tuning, and evaluation techniques.
  • Package and deploy models using containerization (Docker, Kubernetes).
  • Automate and monitor ML workflows using MLflow, Airflow, or cloud-native tools (SageMaker, Vertex AI, Azure ML).
  • Collaborate with data scientists, engineers, and product teams to translate business problems into ML solutions.
  • Contribute to MLOps practices: model versioning, CI/CD for ML, performance monitoring, and rollback strategies.

Requirements

  • 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 (LLMs, embedding models, prompt engineering, 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.