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, intelligent chatbots, 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 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.

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