Machine Learning / GenAI Engineer
Description
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.