Machine Learning & GenAI Engineer

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
• Strong problem-solving ability, analytical mindset, and effective communication skills.
• Ability to work in cross-functional teams and manage multiple priorities in a fast-paced environment.