Machine Learning Engineer

Johnson Electric is one of the world’s largest providers of motion solutions, supplying nearly every major brand in automotive, industrial, and consumer markets. Our Smart Factory initiative leverages advanced analytics, AI and IIoT to drive zero-defect quality, lights-out manufacturing, and sustainable operations across 30+ plants on five continents.

You will be joining a high-impact, hands-on CoE team that owns the full analytical stack: from edge data acquisition and cloud ingestion to model deployment and smart factory adoption.

Accelerate delivery of production-grade AI use-cases. You will prototype, train, and deploy models (classical as well as deep learning) on top of Cloud services and ensure they stay reliable at scale.

Key Responsibilities

Use-Case Delivery

• Translate business problems into ML tasks: predictive maintenance, image segmentation and classification, price quotation and forecasting, etc.
• Build data pipelines (PySpark, Synapse, Databricks) and feature engineering workflows.
• Train, fine-tune, and evaluate ML models (scikit-learn, XGBoost, PyTorch, TensorFlow) following experiment-tracking standards (MLflow).

Model Deployment & Lifecycle Management

• Containerize models; Deploy to AKS/edge devices via automated CI/CD pipelines (AML pipelines, Azure DevOps).
• Establish monitoring suite (Prometheus, Grafana, PromptFlow) for model, and data drift.
• Apply best-practice MLOps patterns: provenance, reproducibility, automated retraining, and rollback strategies.

Collaboration & Agile Delivery

• Co-create user stories with product owners, size tasks, and deliver incremental value in sprints.
• Produce clean, test-covered, well-documented code; participate in peer reviews.
• Conduct workshops and demos to upskill factory engineers & operators.

Qualifications

• 3 – 5 years hands-on experience in ML engineering or data science deploying models to production.
• Solid foundation in traditional ML, statistics, and experimentation (p-values, A/B, power analysis).
• Solid Python programming; experience with unit/integration testing frameworks (pytest).
• Practical knowledge of containerization (Docker) and at least basic Kubernetes concepts (pods, services, config-maps, secrets).
• Familiarity with Azure ML or comparable cloud ML services.
• Familiarity with Generative frameworks like LangChain, LlamaIndex etc to implement Agentic Flows
• Understanding CI/CD & IaC workflows (Git, GitHub Actions or Azure DevOps, Terraform/Bicep).
• Strong communication skills, curiosity to learn manufacturing processes, and bias for hands-on problem solving.