Associate Engineer.RDE.Software.General

## What you\u2019ll do:\n\nEaton Corporation\u2019s 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. \n\nAbout Eaton: \nEaton is power management company with 2018 sales of $21.6 billion. We make what matters work. Everywhere you look\u2014from the technology and machinery that surrounds us, to the critical services and infrastructure that we depend on every day\u2014you\u2019ll find one thing in common. It all relies on power. That\u2019s why Eaton is dedicated to improving people\u2019s 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\u2019re ethical, passionate, accountable, efficient, transparent, and we\u2019re committed to learning. These values enable us to tackle some of the toughest challenges on the planet, never losing sight of what matters. \n \n\u2022 Maintain the infrastructure and tools required to deploy machine learning models at scale. \n\u2022 Develop and maintain Data Engineering pipelines, continuous integration, and deployment (CI/CD) pipelines for machine learning models. \n\u2022 Develop, train, and validate machine learning models to address business needs. \n\u2022 Understanding the challenges in productionizing machine learning software and collaborating with data scientists to ensure that the software best practices, templates and other MLOps principles are integrated to reduce cycle time \n\u2022 Develop and maintain documentation and training materials for machine learning solutions. \n\u2022 Keep up to date with emerging technologies and trends in machine learning and cloud infrastructure.\n\n## Qualifications:\n\n\u2022 Requires a minimum of a Bachelor level degree in computer science or equivalent software engineering discipline. \n \n \n\n\n## Skills:\n\nFresher with understanding in machine learning, software engineering, or related field.\n\n\u2022 Understanding of machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn. \n\u2022 Understanding of cloud infrastructure such as AWS, Azure, or GCP. \n\u2022 Understanding of writing data engineering pipeline code with coding best practices. \n\u2022 Understanding of CI/CD pipelines and containerization technologies such as Docker and Kubernetes. \n\u2022 Understanding of algorithms such as regression, classification, clustering and deep learning. \n\u2022 Understanding of software engineering best practices, including version control, testing, and deployment. \n\u2022 Strong analytical and problem-solving skills. \n\u2022 Excellent communication skills and ability to work collaboratively with other teams. \n\u2022 Ability to manage multiple projects and priorities in a fast-paced environment.\n\nDesired Expertise (in one or more of the following areas): \n\n\n\u2022 Professional certification in Machine Learning or related field. \n\u2022 Understanding of data engineering and data warehousing concepts. \n\u2022 Understanding of big data technologies such as Hadoop, Spark, or Kafka. \n\u2022 Familiarity with DevOps practices and tools. \n\u2022 Familiarity with monitoring and logging tools such as ELK, Grafana, or Prometheus. \n\u2022 Familiarity with Agile methodologies.\n