ML Engineer

4 - 6 years

4 - 7 Lacs

Posted:1 day ago| Platform: Naukri logo

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Job Type

Full Time

Job Description

  1. Collect, clean, and analyze large datasets from production systems (MES, ERP, SCADA, IoT sensors).
  2. Design, develop, and deploy machine learning and deep learning models for predictive maintenance, defect detection, demand forecasting, and production optimization.
  3. Build scalable end-to-end ML pipelines including data preprocessing, feature engineering, training, evaluation, and deployment.
  4. Work with time-series, sensor, image, and quality inspection data to solve manufacturing challenges.
  5. Collaborate with manufacturing, engineering, and operations teams to integrate AI/ML models into real-time production environments.
  6. Implement MLOps practices for model deployment, monitoring, and retraining in production.
  7. Develop computer vision solutions for defect detection, quality inspection, and automation.
  8. Apply advanced optimization and reinforcement learning techniques to improve production scheduling and supply chain processes.
  9. Ensure scalability, security, and performance of AI/ML systems deployed on cloud or edge environments.
  10. Stay up to date with the latest trends in AI, ML, and Industry 4.0 technologies and recommend adoption strategies.
Required Qualifications:
  1. Bachelor s or Master s degree in Computer Science, Data Science, AI/ML, or related field.
  2. 4 6 years of experience as an AI/ML Engineer, Data Scientist, or Machine Learning Engineer, preferably in the manufacturing or industrial domain.
  3. Strong proficiency in Python, and a preferred library like PyTorch, or TensorFlow.
  4. Solid experience with data pipelines and ETL frameworks.
  5. Strong understanding of statistical modeling, deep learning (CNNs, RNNs, Transformers), and classical ML methods.
  6. Hands-on experience with time-series forecasting, anomaly detection, and computer vision.
  7. Familiarity with manufacturing systems (ERP, MES, SCADA, IoT) and their data sources.
  8. Proficiency in SQL and NoSQL databases.
  9. Excellent communication skills with the ability to explain complex AI/ML solutions to both technical and non-technical stakeholders

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