IT engineer Machine Learning
Core
Design, build, and deploy production-grade machine learning models and pipelines to solve business challenges like forecasting, classification, and optimization on the Azure Databricks platform.
Role type
Machine Learning Engineer (MLOps focus)
Builds
Scalable, maintainable, and governed ML pipelines and production models
Domain
Automotive/Industrial technology, Data & Analytics
Deliverable
production ML models
Required skills
Python, scikit-learn, XGBoost, PyTorch, TensorFlow, Pandas, PySpark, MLflow, CI/CD, model versioning, feature engineering, software engineering best practices
Preferred skills
Azure Data & AI certification, Databricks certification, MLflow Professional certification
Technologies
Azure Databricks, MLflow, Azure Blob Storage, Azure Key Vault, Azure Event Hub, Azure API Management, Azure Functions, Azure Event Grid
Responsibilities
Design, build, and evaluate ML models; perform feature engineering; collaborate with data engineers on data acquisition; package and deploy models to production; manage model versioning, monitoring, and lifecycle workflows; build retraining pipelines; integrate ML workflows with Azure-native services; advise Product Owners on ML feasibility; translate business problems into ML workflows; write clean, reusable, testable code for ML pipelines.
Seniority
Mid-level (3–5+ years experience), hands-on IC