MACHINE LEARNING ENGINEER

Core Responsibilities:

o To design, develop, and deploy advanced AI/ML and Generative AI (GenAI) solutions that optimize manufacturing operations in a high-volume drive production environment.

o 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.

2. 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.

2. 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.

2. 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.

2. 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.

o 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:

o Technical Expertise

o Proficiency in Python, R, or Java experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn).

o Strong knowledge of machine learning algorithms, deep learning architectures, and statistical methods.

o Familiarity with MLOps tools (MLflow, KServe, Docker, Kubernetes) and CI/CD pipelines.

o Domain Knowledge

o Understanding of manufacturing processes, MES systems, and industrial automation technologies.

o Experience with predictive maintenance, anomaly detection, and real-time analytics.

o Data Handling

o Expertise in data preprocessing, feature engineering, and working with large-scale sensor/IoT datasets.

o Knowledge of SQL/NoSQL databases and cloud platforms for data storage and model deployment.

o Soft Skills

o Strong problem-solving ability, analytical mindset, and effective communication skills.

o Ability to work in cross-functional teams and manage multiple priorities in a fast-paced environment.