An AI agent that applies to roles like this on employers' own sites. 25 applications free, no card needed.
Job Description
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.
Skills
PythonMachine LearningDeep LearningTensorFlowPyTorchScikit-learnData EngineeringMLOpsFeature EngineeringApache SparkHadoopKafkaAirflowSnowflakeDatabricksIf a job posting appears fraudulent, asks for payment, contains misleading information, or violates our guidelines, please report it immediately. Our team will review it promptly, Jobaaj does not charge any fee from the applicants.
About Company
In 1911, Joseph O. Eaton, brother-in-law Henning O. Taube and Viggo V. Torbensen, incorporated the Torbensen Gear and Axle Co. in Bloomfield, New Jersey. With financial backing from Torbensen's mother, the company was set to manufacture Torbensen's patented internal-gear truck axle. In 1914, the company moved to Cleveland, Ohio, to be closer to its core business, the automotive industry.
The Torbensen Axle Company incorporated in Ohio in 1916, succeeding the New Jersey corporation. A year later, Republic Motor Truck Company, Torbensen's largest customer bought out the company. But Eaton and Torbensen were not content and bowed out of Republic to form the Eaton Axle Company in 1919. A year later, in 1920, Eaton Axle Company merged with Standard Parts. Standard Parts went in receivership later the same year and was later liquidated. In 1923, Eaton bought the Torbensen Axle Co. back from Republic and changed the name to the Eaton Axle and Spring Company.
Eaton officers believed the quickest way to grow the business was through acquisitions and began buying companies in the automotive industry. By 1932, the diversified company changed its name to Eaton Manufacturing Company. In 1937, Eaton became international by opening a manufacturing plant in Canada. In 1958 Eaton Corporation acquired Fuller Manufacturing. The company name changed once again in 1965 to Eaton Yale & Towne Inc. after the acquisition of Yale & Towne Manufacturing Co. in 1963. Stockholders approved the change to the company's current name in 1971. In 1978, Eaton Corporation acquired Samuel Moore & Company, Kenway Systems, and Cutler-Hammer.
Similar Jobs
View all
New Data Science Analytics & Machine Learning roles in Hadapsar.
One email a day. Unsubscribe any time.
