Machine Learning Engineer
Job Description
Johnson Electric, a global leader in motion solutions with over 35,000 employees across 22 countries, is seeking a Machine Learning Engineer to join their Smart Factory initiative. This role is part of a high-impact Center of Excellence team that manages the complete analytical stack, from edge data acquisition to model deployment. The position offers an opportunity to work on cutting-edge AI applications in manufacturing, implementing solutions for predictive maintenance, computer vision, and advanced analytics.
The ideal candidate will be responsible for developing and deploying production-grade AI solutions, building robust data pipelines, and implementing MLOps best practices. You'll work with modern tools and technologies including PyTorch, TensorFlow, Azure ML, and Kubernetes, while collaborating with cross-functional teams to drive zero-defect quality and lights-out manufacturing across 30+ global plants.
This role combines technical expertise in machine learning with practical industrial applications, offering the chance to make a significant impact on global manufacturing operations. The company provides an inclusive and diverse workplace environment, emphasizing employee growth and meaningful work. As part of Johnson Electric's digital transformation journey, you'll be at the forefront of implementing AI solutions that improve manufacturing efficiency and quality across a global scale.
The position requires strong technical skills in Python, ML frameworks, and cloud services, combined with the ability to communicate effectively with stakeholders and translate complex technical concepts into business value. This is an excellent opportunity for a mid-level ML engineer looking to work on real-world AI applications in a global manufacturing context.
Responsibilities For Machine Learning Engineer
- Translate business problems into ML tasks including predictive maintenance, image segmentation and classification, price quotation and forecasting
- Build data pipelines using PySpark, Synapse, Databricks and feature engineering workflows
- Train, fine-tune, and evaluate ML models using scikit-learn, XGBoost, PyTorch, TensorFlow
- Containerize models and deploy to AKS/edge devices via automated CI/CD pipelines
- Establish monitoring suite for model and data drift
- Apply MLOps best practices
- Collaborate with product owners and deliver value in sprints
- Produce clean, test-covered, well-documented code
Requirements For Machine Learning Engineer
- 3-5 years hands-on experience in ML engineering or data science deploying models to production
- Solid foundation in traditional ML, statistics, and experimentation
- Solid Python programming experience with unit/integration testing frameworks
- Practical knowledge of containerization and basic Kubernetes concepts
- Familiarity with Azure ML or comparable cloud ML services
- Familiarity with Generative frameworks like LangChain, LlamaIndex
- Understanding of CI/CD & IaC workflows
- Strong communication skills and hands-on problem solving
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