Responsibilities
1. Design and optimize ML models for time series forecasting, anomaly detection, and computer vision (e.g., quality inspection)
2. Apply advanced techniques such as LSTM, CNN, XGBoost, PCA, clustering, and Explainable AI (e.g., SHAP, LIME)
3. Analyze structured and unstructured data from operational and sensor systems
4. Develop and maintain robust ML pipelines for training, validation, deployment, and monitoring
5. Collaborate with data engineers, solution architects, and IoT/Edge teams to ensure scalable integration
6. Use SQL and BI tools (e.g., Power BI) to prepare and visualize large datasets
7. Work with cloud platforms (e.g., AWS, Azure, GCP) for model deployment and MLOps lifecycle management
Your Profile:
8. 3+ years of hands-on experience developing and deploying ML models in production environments
9. Strong Python skills and experience with ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch)
10. Proficient in data preprocessing, feature engineering, and model validation techniques
11. Familiarity with Explainable AI (XAI), data governance, and ethical AI principles
12. Solid SQL skills and experience with BI/dashboard tools such as Power BI
13. Experience working with cloud-based ML services (e.g., SageMaker, Azure ML, Vertex AI)
14. Strong communication skills and ability to explain technical results to both technical and business stakeholders
Preffered Qualifications
15. Experience in Industry 4.0, manufacturing, or process automation projects
16. Familiarity with real-time/streaming data platforms, time series databases, and tools like Grafana
17. Understanding of MLOps pipelines and tools for automated model management and deployment
18. Experience with orchestration tools such as Apache Airflow or dbt
Application Process