Machine Learning / GenAI Engineer

Machine Learning/GenAI Engineer x2 – Focus: Smart Manufacturing & AI Solutions 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. o   Model Development & Deployment § Build and implement machine learning models for predictive maintenance, anomaly detection, and process optimization. § Develop GenAI-powered applications for automated reporting, intelligentchatbots, and simulation of manufacturing scenarios. § Translate research-level algorithms into production-ready solutions using MLOps best practices. o   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. o   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. o   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 solutionswith factory goals. § Communicate insights and recommendations to stakeholders throughdashboards 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 learningarchitectures, and statistical methods. o  Familiarity with MLOps tools (MLflow, KServe, Docker, Kubernetes) andCI/CD pipelines. o  Domain Knowledge o  Understanding of manufacturing processes, MES systems, and industrialautomation technologies. o  Experience with predictive maintenance, anomaly detection, and real-timeanalytics. o  Data Handling o  Expertise in data preprocessing, feature engineering, and working withlarge-scale sensor/IoT datasets. o  Knowledge of SQL/NoSQL databases and cloud platforms for data storageand model deployment. o  Soft Skills o  Strong problem-solving ability, analytical mindset, and effectivecommunication skills. Ability to work in cross-functional teams and manage multiple priorities in a fast-paced environment.