Weare seeking experienced Machine Learning / Generative AI Engineer todesign, develop, and deploy advanced AI/ML and Generative AI (GenAI) solutionsthat optimize manufacturing operations in a high-volume production environment.
Thisrole focuses on leveraging machine learning, predictive analytics, andautomation technologies to improve production yield, reduce downtime, andenable smart factory capabilities aligned with Industry 4.0 principles.
KeyResponsibilities
ModelDevelopment & Deployment
- Design, build, and deploy machine learning models for predictive maintenance, anomaly detection, and process optimization.
- Develop GenAI-powered applications, including automated reporting tools, intelligent chatbots, and manufacturing scenario simulations.
- Translate research-oriented algorithms into scalable, production-ready solutions using MLOps best practices.
DataEngineering & 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 Manufacturing Execution Systems (MES) to support real-time decision-making.
PredictiveAnalytics & Quality Control
- Apply AI and machine learning techniques to forecast equipment failures, optimize production schedules, and improve product quality.
- Utilize computer vision and deep learning technologies for automated defect detection and quality assurance.
Automation& Continuous Improvement
- Implement AI-driven workflows and GenAI-based conversational assistants to reduce manual intervention and improve operational efficiency.
- Monitor model performance, detect model drift, and automate retraining processes to maintain model accuracy and reliability.
Collaboration& Reporting
- Collaborate closely with engineering and IT teams to align AI and GenAI initiatives with manufacturing objectives.
- Communicate insights, recommendations, and performance metrics through dashboards and GenAI-generated natural language summaries.
Required Skills
TechnicalExpertise
- Proficiency in programming languages such as Python, R, or Java.
- Hands-on experience with machine learning frameworks, including TensorFlow, PyTorch, and Scikit-learn.
- Strong understanding of machine learning algorithms, deep learning architectures, and statistical analysis methods.
- Familiarity with MLOps tools and platforms such as MLflow, KServe, Docker, and Kubernetes.
- Experience with CI/CD pipelines and deployment automation.
DomainKnowledge
- Understanding manufacturing processes, MES systems, and industrial automation technologies.
- Experience in predictive maintenance, anomaly detection, and real-time analytics within manufacturing environments.
DataEngineering & Data Handling
- Expertise in data preprocessing, feature engineering, and handling large-scale sensors and IoT datasets.
- Knowledge of SQL and NoSQL databases, as well as cloud platforms used for data storage and model deployment.
SoftSkills
- Strong analytical thinking and problem-solving capabilities.
- Effective verbal and written communication skills.
- Ability to work collaboratively in cross-functional teams and manage multiple priorities in a fast-paced environment.