We are looking for a Machine Learning Engineering Intern to build a predictive maintenance solution that anticipates equipment failures before they happen. The goal is to help industrial teams reduce downtime and maintenance costs by turning sensor data into early warnings and clear recommendations.
The project involves building a solution capable of:
- Collecting and cleaning sensor data through a pipeline with automated quality checks.
- Forecasting equipment behaviour and detecting anomalies on time-series data.
- Estimating failure risk and raising alerts with severity levels.
- Showing asset health, trends and alerts in a monitoring dashboard.
Technical Skills
- Solid Python and data libraries (pandas, NumPy, scikit-learn).
- Understanding of time-series analysis and forecasting.
- Basic knowledge of SQL and data pipelines.
- Familiarity with Git and Docker.
Tech Stack
- Data & ML: Python, pandas, scikit-learn, Prophet or PyTorch
- Storage: PostgreSQL or TimescaleDB
- MLOps: MLflow, Evidently, Docker
- Dashboards: Grafana or Streamlit
- Tools: Git, GitHub Actions
