AI / ML Engineer (Smart Manufacturing & GenAI Solutions)
- Compensation
- Not disclosed
- Location
- Not listed
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- 5-10 years of relevant working experience.
- Proficiency in programming languages such as Python, R, or Java, with hands-on experience using machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn.
- Strong understanding of machine learning algorithms, deep learning architectures, and statistical analysis techniques.
- Experience with MLOps tools and platforms such as MLflow, KServe, Docker, and Kubernetes, along with familiarity in implementing CI/CD pipelines.
Full description
AI / ML & GenAI Solution Development
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Design, develop, and deploy AI/ML and Generative AI solutions to optimize high-volume drive manufacturing operations.
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Build machine learning models for predictive maintenance, anomaly detection, yield improvement, and process optimization.
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Develop GenAI-powered applications such as automated reporting tools, intelligent chatbots, and manufacturing scenario simulations.
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Convert research-based algorithms into scalable, production-ready solutions following MLOps best practices.
Data Engineering & System Integration
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Develop and maintain data pipelines for sensor, MES, and IoT data ingestion, transformation, and model inference.
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Integrate AI models with MES and factory control systems to enable real-time decision-making.
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Ensure seamless deployment within CI/CD and containerized environments.
Predictive Analytics & Quality Engineering
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Implement AI-driven predictive analytics to forecast equipment failures and optimize production scheduling.
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Apply computer vision and deep learning techniques for automated defect detection and quality assurance.
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Enhance product quality and reduce downtime using data-driven insights.
Automation & Continuous Optimization
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Deploy AI-driven workflows and GenAI conversational assistants to reduce manual intervention.
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Monitor model performance, detect model drift, and automate retraining pipelines.
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Continuously improve AI systems aligned with Industry 4.0 smart factory initiatives.
- Collaborate with manufacturing engineers, automation teams, and IT stakeholders.
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Present AI-driven insights through dashboards and GenAI-generated summaries.
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Align AI solutions with overall factory performance goals.
Requirements:
- 5-10 years of relevant working experience.
- Proficiency in programming languages such as Python, R, or Java, with hands-on experience using machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn.
-
Strong understanding of machine learning algorithms, deep learning architectures, and statistical analysis techniques.