Machine Learning Engineer
Description For Machine Learning Engineer
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 exciting opportunity to work on production-grade AI use-cases, leveraging advanced analytics and IIoT to achieve zero-defect quality and lights-out manufacturing across 30+ global plants. You'll be responsible for developing and deploying machine learning solutions for predictive maintenance, image processing, and forecasting applications.
As a Machine Learning Engineer, you'll work with cutting-edge technologies including PyTorch, TensorFlow, and cloud services while implementing MLOps best practices. The role combines technical expertise in ML engineering with practical applications in manufacturing, offering a unique opportunity to directly impact industrial automation and smart factory operations.
The ideal candidate should have 3-5 years of hands-on ML engineering experience, strong Python programming skills, and familiarity with modern MLOps practices. You'll be joining a global, inclusive team that values diversity and innovation, working in an environment that emphasizes continuous learning and professional growth.
This role offers the chance to work with a company that's at the forefront of manufacturing innovation, serving major brands in automotive, industrial, and consumer markets. You'll be part of a team that's driving digital transformation in manufacturing while contributing to Johnson Electric's mission of improving quality of life through innovative motion systems.
Responsibilities For Machine Learning Engineer
- Translate business problems into ML tasks: 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 CI/CD & IaC workflows
- Strong communication skills and hands-on problem solving
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