Engineer - ML

What You’ll Do
Eaton Corporation’s Center for Intelligent Power has an opening for a Associate Engineer- Machine Learning Operations. The ideal candidate will be responsible for developing and maintaining the infrastructure and tools required to deploy and maintain machine learning models at scale. This position requires understanding in machine learning and software engineering. The candidate will work closely with other teams to make sure the requested features by the businesses are delivered.
About Eaton
Eaton is power management company with 2018 sales of $21.6 billion. We make what matters work. Everywhere you look—from the technology and machinery that surrounds us, to the critical services and infrastructure that we depend on every day—you’ll find one thing in common. It all relies on power. That’s why Eaton is dedicated to improving people’s lives and the environment with power management technologies that are more reliable, efficient, safe and sustainable. Because this is what matters. We are confident we can deliver on this promise because of the attributes that our employees embody. We’re ethical, passionate, accountable, efficient, transparent, and we’re committed to learning. These values enable us to tackle some of the toughest challenges on the planet, never losing sight of what matters."
Develop, deploy, and maintain scalable machine learning platforms, infrastructure, and MLOps solutions in cloud environments.
Develop, and optimize data engineering pipelines, feature engineering workflows, and CI/CD pipelines for machine learning and AI applications.
Build, train, validate, deploy, monitor, and continuously improve machine learning and deep learning models in production.
Drive adoption of software engineering best practices, reusable frameworks, templates, coding standards, and automation strategies to reduce development cycle time and improve solution reliability.
Troubleshoot model performance, data quality, pipeline reliability, and production issues while ensuring high availability and scalability.
Contribute to technical design reviews, architecture discussions, and team knowledge-sharing initiatives.
Develop and maintain technical documentation, deployment guides, operational runbooks, and training materials.
Evaluate emerging technologies, AI/ML frameworks, cloud services, and industry best practices, and recommend their adoption where appropriate.
Qualifications
Requires a minimum of a Bachelor level degree in computer science or equivalent software engineering discipline.
3–5 years of professional experience in Machine Learning Engineering, MLOps, Data Engineering, or Software Engineering.
Skills
Hands-on experience building and deploying machine learning solutions in production environments.
Experience designing and developing data pipelines, feature stores, and data processing workflows using industry best practices.
Understanding of CI/CD pipelines, Infrastructure as Code (IaC), and automation frameworks for machine learning systems.
Good understanding of cloud infrastructure such as AWS, Azure, or GCP.
Understanding of containerization and orchestration technologies such as Docker and Kubernetes.
Understanding of machine learning algorithms including regression, classification, clustering, recommendation systems, time-series forecasting, and deep learning.
Experience with model monitoring, drift detection, performance optimization, and observability frameworks.
Strong analytical, troubleshooting, and problem-solving skills.
Excellent communication and stakeholder management skills with the ability to work effectively across cross-functional teams.
Required
Experience with big data technologies such as Apache Spark, Kafka, Hadoop, Flink, or similar distributed systems.
Experience developing and maintaining large-scale data engineering pipelines using Python, Spark, Databricks, Airflow, or similar technologies.
Experience implementing CI/CD pipelines using Azure DevOps, GitHub Actions, Jenkins, or equivalent tools.
Understanding of Docker, Kubernetes, and container-based application deployment.
Strong understanding of software engineering best practices including object-oriented design, design patterns, version control, testing strategies, code reviews, and secure development practices.
Experience with model lifecycle management, experiment tracking, feature management, and production model deployment.
Professional certifications in Machine Learning, Cloud Technologies, Data Engineering, or MLOps.
Hands-on experience with Databricks, Snowflake, Delta Lake, Feature Stores, or Vector Databases.
Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, MLflow, or equivalent platforms.

Work arrangement
Hybrid