Machine Learning Engineer

Join us and contribute to driving excellence at MOTOLITE!

Job Summary:

The Machine Learning Engineer is responsible for designing, building, deploying, and maintaining production-ready machine learning and AI solutions that support manufacturing operations, process improvement, quality enhancement, and digital transformation initiatives. The role focuses on developing scalable end-to-end pipelines and intelligent applications that turn data into practical operational solutions for the plant and the business.

This position supports the implementation of AI use cases such as computer vision, anomaly detection, predictive analytics, forecasting, optimization, and intelligent decision support. It also ensures that AI solutions are securely integrated into production environments and aligned with engineering best practices in reliability, scalability, maintainability, and MLOps.

PRINCIPAL DUTIES

• Design, build, and maintain end-to-end machine learning pipelines covering data preparation, feature engineering, model training, validation, deployment, monitoring, and retraining.
• Develop and operationalize machine learning and AI solutions for manufacturing and business applications, including predictive, prescriptive, and vision-based systems that support process improvement, quality enhancement, automation, and decision support.
• Design, develop, and optimize AI-enabled solutions and model pipelines to ensure they are scalable, reliable, and fit for operational use in manufacturing environments.
• Deploy machine learning models and AI services into production environments using appropriate cloud, on-premise, and containerized platforms, ensuring secure, scalable, and reliable operation.
• Establish and maintain MLOps practices, including versioning, experiment tracking, model registry, CI/CD, workflow orchestration, automated testing, and reproducible deployment processes.
• Monitor deployed models and AI services for performance degradation, data drift, model drift, latency, reliability issues, and operational failures, and implement corrective actions, optimization, or retraining as needed.
• Optimize model training and inference workflows for batch and real-time applications to ensure efficient resource utilization and responsive operational performance.
• Work closely with data engineers, data scientists, software engineers, BI analysts, and manufacturing stakeholders to integrate AI solutions into business processes, digital platforms, and plant operations.
• Apply best practices in data governance, documentation, security, and compliance to ensure that machine learning workflows and AI systems are reliable, transparent, and maintainable.
• Evaluate emerging machine learning, computer vision, and AI technologies to identify opportunities for improving solution capability, scalability, and business impact in manufacturing.
• Perform other related tasks as needed to support enterprise AI, analytics, and digital transformation initiatives.

JOB SPECIFICATIONS

1. Formal Education:

• Graduate of Computer Science, Information Technology, Data Science, Computer Engineering, Electronics Engineering, Industrial Engineering, Applied Mathematics, Statistics, or other related fields.

• Master’s degree or relevant postgraduate studies is an advantage.

2. Experience:

• At least 2–4 years of relevant experience in machine learning engineering, AI development, data science deployment, or related roles.

• Experience in deploying machine learning models into production environments is preferred.

• Experience in computer vision, anomaly detection, predictive analytics, or industrial AI applications is an advantage.

• Experience in manufacturing, industrial, or operational analytics environments is preferred.

3. Training/Skills:

• Proficient in Python and machine learning frameworks such as scikit-learn, TensorFlow, PyTorch, or similar tools.

• Familiar with building and deploying machine learning pipelines for training, inference, monitoring, and retraining.

• Experience in computer vision techniques such as image classification, object detection, segmentation, and visual inspection systems is an advantage.

• Familiar with ML deployment and MLOps tools such as Docker, Git, MLflow, orchestration frameworks, and CI/CD pipelines.

• Knowledgeable in data preprocessing, feature engineering, model evaluation, model monitoring, and performance optimization.

• Familiar with cloud or on-premise environments for AI deployment and scalable model serving.

• Able to work with structured, semi-structured, sensor, image, and time-series data.

• Strong analytical, problem-solving, and debugging skills.

• Strong documentation, stakeholder engagement, and cross-functional collaboration skills.

• Able to translate operational and business problems into practical, scalable, and production-ready AI solutions.

“Motolite offers you not just a job, but a career with boundless opportunities”