Current openings

We are looking for a Machine Learning / AI Engineer to help build, improve, and operationalize AI capabilities and intelligent models within the GovHorizon platform — from data preparation through to production deployment.

• Build, train, and optimize ML/AI models for strategic use cases (e.g. regulatory forecasting, text analysis, predictive intelligence).
• Prepare and clean data, run feature engineering, and validate input quality for models.
• Work with engineering teams to deploy models to production (APIs, MLOps pipelines).
• Monitor model performance and implement automated retraining, drift detection, and performance metrics.
• Document workflows, model architecture, and experiment results.
• Help operationalize MLOps practices and model lifecycle management tools.

• 3–5 years of experience in Machine Learning, Data Science, or AI Engineering.
• Proficiency in Python and ML libraries (scikit-learn, TensorFlow, PyTorch, or equivalent).
• Knowledge of data pipelines, ETL/ELT, and orchestration frameworks (Airflow, Kubeflow, etc.).
• Experience with cloud ML services and deployment (Azure ML, Databricks, AWS SageMaker, GCP Vertex AI).
• Solid understanding of model performance metrics and result validation.

We are looking for an AI Research Trainee to join GovHorizon's R&D team. You will support fine-tuning of language models for explainable scenario generation, working under the scientific coordination of our CTO and with access to European supercomputing infrastructure.

• Support literature review on LLM fine-tuning and explainability methods, and help organize findings relevant to the project.
• Help configure and run experiments on supercomputing infrastructure.
• Contribute to testing source-traceability mechanisms in model outputs.
• Support evaluation and documentation of experiment results.
• Take part in internal technical reviews, following the progress of ongoing R&D work.

• Currently completing, or recently completed, a degree in Machine Learning, Data Science, NLP or a related field.
• Basic proficiency in Python.
• Familiarity with machine learning fundamentals; prior exposure to LLMs is a plus, not a requirement.
• Curiosity about explainability and interest in applied research.