MLOps Engineer

As an MLOps Engineer at RevoData, you will help clients automate and streamline the deployment, monitoring, and maintenance of machine learning models at scale. You’ll combine strong technical skills with a collaborative mindset to build robust, scalable ML platforms that accelerate business value.

• End-to-end ML model lifecycle experience (development to decommissioning) on at least 4 production projects.
• Experience using big data frameworks to build scalable pipelines for model training and inference.
• Working knowledge of LLMs, RAG, and context engineering, with practical experience using platforms such as Hugging Face, Azure 
  AI, and OpenAI.
• Proficiency in deploying both traditional ML models (e.g., LightGBM, XGBoost, Scikit-learn) and deep learning models (e.g., 
  TensorFlow, PyTorch), as well as GenAI tools such as LangChain, LlamaIndex, and Pydantic AI.
• Experience integrating Databricks into ML workflows, including model serving endpoints and Databricks Asset Bundles for CI/CD automation.
• Familiarity with MLflow, including experiment tracking, artifact management, and model registry.
• A focus on automating end-to-end ML workflows using Databricks, ensuring robustness, traceability, and team collaboration. 
• Ability to manage priorities across diverse client projects, with a passion for learning and sharing knowledge.
• Proficiency in Dutch is required, with exceptions for candidates with exceptional Databricks expertise (e.g. Champions or those with proven advanced experience on the platform).

This role is open to Senior and Principal level candidates. For corresponding salary details, please see the Additional Info section at the bottom of this vacancy!