Ml Engineer

Job Overview: We are seeking a skilled and proactive Machine Learning Engineer to join our smart manufacturing initiative. You will play a pivotal role in building data pipelines, developing ML models for defect prediction, and implementing closed-loop control systems to improve production quality. Responsibilities: Data Engineering & Pipeline Support: Validate and ensure correct data flow from Influx DB/CDL to Smart box/Databricks platforms. Collaborate with data scientists to support model development through accurate data provisioning. Provide ongoing support in resolving data pipeline issues and performing ad-hoc data extractions. ML Model Development: Develop three distinct ML models to predict different types of defects using historical production data. Predict short-term outcomes (next 5 minutes) using techniques like artificial sampling and dimensionality reduction. Ensure high model performance: Accuracy 95%, Precision & Recall 80%. Extract and present feature importance to support model interpretability. Closed-loop Control Architecture: Implement end-to-end ML-driven automation to proactively correct machine settings based on model predictions. Key architecture components include: Real-time data ingestion from PLCs via Influx DB/CDL. Model deployment and inference on Smart box. Output pipeline to share actionable recommendations via PLC tags. Automated retraining pipeline in the cloud triggered by model drift or recommendation deviations. Qualifications: Proven experience with real-time data streaming from industrial systems (PLCs, Influx DB/CDL). Hands-on experience in building and deploying ML models in production. Strong understanding of data preprocessing, dimensionality reduction, and synthetic data techniques. Familiarity with cloud-based retraining workflows and model performance monitoring. Experience in smart manufacturing or predictive maintenance is a plus.