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.