AI / ML Engineer

Machine Learning/GenAI Engineer Focus: Smart Manufacturing & AI Solutions

Core Responsibilities:

oTo design, develop, and deploy advanced AI/ML and Generative AI (GenAI)solutions that optimize manufacturing operations in a high-volume drive production environment.

oThis 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.

oModel Development & Deployment

Build and implement machine learning models for predictive maintenance, anomaly detection, and process optimization.

DevelopGenAI-powered applications for automated reporting, intelligent chatbots, and simulation of manufacturing scenarios.

Translate research-level algorithms into production-ready solutions using MLOps best practices.

oData 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.

oPredictive 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.

oAutomation & 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.

oCollaboration & 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 andnatural language summaries generated by GenAI.

Required Skills:

oTechnical Expertise

oProficiency in Python, R, or Java; experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn).

oStrong knowledge of machine learning algorithms, deep learning architectures, and statistical methods.

oFamiliarity with MLOps tools (MLflow, KServe, Docker, Kubernetes) and CI/CD pipelines.

oDomain Knowledge

oUnderstanding of manufacturing processes, MES systems, and industrial automation technologies.

oExperience with predictive maintenance, anomaly detection, and real-time analytics.

oData Handling

oExpertise in data preprocessing, feature engineering, and working with large-scale sensor/IoT datasets.

oKnowledge of SQL/NoSQL databases and cloud platforms for data storage and model deployment.

oSoft Skills

oStrong 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