AI / ML Engineer.
The role focuses on designing, building, and deploying production‑grade machine learning models on Azure using MLOps practices to support predictive and forecasting use cases. The engineer will collaborate on an international enterprise analytics project, ensuring scalable and reliable AI solutions for business decision‑making.
Skills required
About this role
Are you passionate about building production-ready AI and machine learning solutions? Do you enjoy solving complex business problems using predictive models and advanced analytics? Would you like to contribute to an international project focused on enterprise analytics and AI innovation? Then we have an exciting opportunity for you!
The international IT company Intetics is looking for an experienced AI / ML Engineer to join our team.
The role involves developing, deploying, and maintaining machine learning solutions that support predictive analytics, forecasting, and AI-driven decision-making. You will work with modern Azure technologies, enterprise data platforms, and MLOps practices to deliver scalable, reliable, and business-focused AI applications.
Requirements
- Design, develop, and deploy production-ready machine learning models.
- Build predictive, forecasting, and probabilistic models for business use cases.
- Perform feature engineering, data preparation, and model validation.
- Monitor model performance and ensure reproducibility and reliability.
- Integrate ML solutions with enterprise data platforms and BI applications.
- Implement MLOps practices, automated testing, and deployment pipelines.
- Collaborate with cross-functional teams to deliver AI-driven business solutions.
Requirements
- Strong Python programming skills for machine learning development.
- Experience deploying machine learning models into production environments.
- Experience with forecasting, predictive analytics, or probabilistic modeling.
- Knowledge of feature engineering, model validation, and performance monitoring.
- Experience with MLOps practices and automated deployment.
- Understanding of explainable and responsible AI principles.
- Experience integrating ML solutions with enterprise data platforms.
Nice to Have:
- Experience with Monte Carlo simulations or other stochastic methods.
- Experience with Azure Machine Learning, Microsoft Fabric Data Science, or MLflow.
- Experience with GenAI, NLP, or Retrieval-Augmented Generation (RAG) solutions.
- Experience with workforce, finance, or cost forecasting projects.
