Job Description :
Machine Learning/GenAI Engineer x2 – Focus: Smart Manufacturing & AI Solutions
Core Responsibilities:
- To design, develop, and deploy advanced AI/ML and Generative AI (GenAI) solutions that optimize manufacturing operations in a high-volume drive production environment.
- This role focuses on leveraging machine learning, predictive analytics, and automation to improve yield, reduce downtime, and enable smart factory capabilities aligned with Industry 4.0 principles.
Model Development & Deployment
- Build and implement machine learning models for predictive maintenance, anomaly detection, and process optimization.
- Develop GenAI-powered applications for automated reporting, intelligent chatbots, and simulation of manufacturing scenarios.
- Translate research-level algorithms into production-ready solutions using MLOps best practices.
Data Engineering & Integration
- Develop robust data pipelines to collect, clean, and transform sensor, MES, and IoT data for model training and inference.
- Integrate AI models with factory control systems and MES for real-time decision-making.
Predictive Analytics & Quality Control
- Apply AI techniques to forecast equipment failures, optimize production schedules, and enhance product quality.
- Use computer vision and deep learning for automated defect detection and quality assurance.
Automation & Continuous Improvement
- Implement AI-driven workflows and GenAI-based conversational assistants to reduce manual interventions and accelerate cycle times.
- Monitor model performance, detect drift, and automate retraining processes.
Collaboration & Reporting
- Work closely with engineers and IT teams to align AI and GenAI solutions with factory goals
- Communicate insights and recommendations to stakeholders through dashboards and natural language summaries generated by GenAI.
Required Skills:
Technical Expertise
- Proficiency in Python, R, or Java; experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn).
- Strong knowledge of machine learning algorithms, deep learning architectures, and statistical methods
- Familiarity with MLOps tools (MLflow, KServe, Docker, Kubernetes) and CI/CD pipelines. Domain Knowledge
- Understanding of manufacturing processes, MES systems, and industrial automation technologies.
- Experience with predictive maintenance, anomaly detection, and real-time analytics. Data Handling
- Expertise in data preprocessing, feature engineering, and working with large-scale sensor/IoT datasets.Knowledge of SQL/NoSQL databases and cloud platforms for data storage and model deployment. Soft Skills