Model Development & Deployment
- Architect, train, and operationalize machine learning models for predictive maintenance, anomaly detection, and manufacturing process optimization.
- Build Generative AI applications such as automated reporting systems, intelligent virtual assistants, and production scenario simulation tools.
- Convert experimental AI/ML algorithms into scalable, production-grade solutions by applying MLOps best practices.
Data Engineering & Integration
- Design and maintain reliable data pipelines to ingest, cleanse, and transform sensor, MES, and IoT data for model training and real-time inference.
- Integrate AI solutions with factory automation systems and Manufacturing Execution Systems (MES) to enable data-driven operational decisions.
Predictive Analytics & Quality Control
- Leverage machine learning and AI techniques to predict equipment breakdowns, optimize manufacturing schedules, and enhance product quality.
- Implement computer vision and deep learning solutions for automated defect identification and quality inspection processes.
Automation & Continuous Improvement
- Develop AI-powered automation workflows and GenAI conversational solutions to minimize manual processes and improve operational productivity.
- Track model performance, identify model drift, and streamline automated retraining workflows to ensure sustained model reliability and accuracy.
Collaboration & Reporting
- Partner with engineering, operations, and IT teams to align AI and GenAI initiatives with broader manufacturing goals.
- Present actionable insights, recommendations, and performance results through dashboards and AI-generated narrative reports.
Required Skills
- At least 7 years of relevant working experience.
- Demonstrated proficiency in programming languages such as Python, R, or Java.
- Practical experience working with machine learning frameworks including TensorFlow, PyTorch, and Scikit-learn.
- Strong knowledge of machine learning methodologies, deep learning models, and statistical analysis techniques.
- Familiarity with MLOps technologies and platforms such as MLflow, KServe, Docker, and Kubernetes.
- Experience implementing CI/CD workflows and deployment automation practices.
Domain Knowledge
- Solid understanding of manufacturing operations, MES platforms, and industrial automation systems.
- Hands-on experience applying predictive maintenance, anomaly detection, and real-time analytics in manufacturing environments.
Data Engineering & Data Handling
- Expertise in data cleansing, feature engineering, and managing high-volume sensor and IoT datasets.
Working knowledge of SQL, NoSQL databases, and cloud-based infrastructure for data storage and model deployment.
Soft Skills
- Excellent analytical and critical-thinking abilities with a strong problem-solving mindset.
- Strong written and verbal communication skills with the ability to explain technical concepts clearly.
- Proven ability to collaborate effectively across cross-functional teams while managing competing priorities in dynamic environments.
To apply, please visit and search for Job Reference:
We regret that only shortlisted candidates will be notified.
GMP Technologies (S) Pte Ltd | EA License: 11C3793 | EA Personnel: Bautista Gia Grace De Guzman | Registration No: R23111973
