DevOps Engineer AI Application Development

Job Description

As a DevOps Engineer at Emerson, you will be responsible for overseeing the end-to-end lifecycle of machine learning models, from deployment to monitoring and maintenance. You will work closely with data scientists, machine learning engineers, and development teams to ensure that ML models are efficiently integrated into production systems and deliver high performance.**Your Responsibilities Will Be:**- Deploy and handle machine learning models in production environments, ensuring they are scalable, reliable, and performant.- Design and implement CI/CD (Continuous Integration/Continuous Deployment) pipelines for ML models to streamline development and deployment processes.- Develop and maintain the infrastructure required for model deployment, including containerization (e.g., Docker), orchestration (e.g., Kubernetes), and cloud services (e.g., AWS, Google Cloud, Azure).- Supervise the performance of deployed models, seek issues, and perform regular maintenance to ensure models remain accurate and effective.- Make sure that model deployment and data handling align with security and regulatory requirements.- Implement standard methodologies for data privacy and protection.- Create and maintain documentation for deployment processes, model performance, and system configurations.- Deliver clear and detailed reports to collaborators.- Identify and implement improvements to model performance, deployment processes, and infrastructure efficiency.- Participate in regular Scrum events such as Sprint Planning, Sprint Review, and Sprint Retrospective.**For This Role, You Will Need:**- Bachelors degree in computer science, Data Science, Statistics, or a related field or equivalent experience is acceptable.- Total 7 years of confirmed experience.- More than tried ability in ML Ops, DevOps, or a related role, with a confirmed understanding of deploying and handling machine learning models in production environments.- Experience with containerization technologies (e.g., Docker) or equivalent and orchestration platforms (e.g., Kubernetes).- Familiarity with cloud services Azure and AWS and their ML offerings.- Experience with CI/CD tools and practices for automating deployment pipelines (e.g., Azure Pipeline, Azure DevOps).- Experience with supervising and logging tools to supervise model performance and system health.**Preferred Qualifications that Set You Apart:**- Prior experience in engineering domain and working with teams in Scaled Agile Framework (SAFe) are nice to have.- Knowledge of data engineering and ETL (Extract, Transform, Load) processes.- Experience with version control systems (e.g., Git) and collaboration tools.- Understanding of machine learning model life cycle management and model versioning.At Emerson, they prioritize a workplace where every employee is valued, respected, and empowered to grow. They foster an environment that encourages innovation, collaboration, and diverse perspectives. Their commitment to ongoing career development and growing an inclusive culture ensures you have the support to thrive. Whether through mentorship, training, or leadership opportunities, they invest in your success so you can make a lasting impact. Emerson believes diverse teams working together are key to driving growth and delivering business results. They recognize the importance of employee well-being and prioritize providing competitive benefits plans, a variety of medical insurance plans, Employee Assistance Program, employee resource groups, recognition, and much more. Their culture offers flexible time off plans, including paid parental leave (maternal and paternal), vacation, and holiday leave. As a DevOps Engineer at Emerson, you will be responsible for overseeing the end-to-end lifecycle of machine learning models, from deployment to monitoring and maintenance. You will work closely with data scientists, machine learning engineers, and development teams to ensure that ML models are efficiently integrated into production systems and deliver high performance.**Your Responsibilities Will Be:**- Deploy and handle machine learning models in production environments, ensuring they are scalable, reliable, and performant.- Design and implement CI/CD (Continuous Integration/Continuous Deployment) pipelines for ML models to streamline development and deployment processes.- Develop and maintain the infrastructure required for model deployment, including containerization (e.g., Docker), orchestration (e.g., Kubernetes), and cloud services (e.g., AWS, Google Cloud, Azure).- Supervise the performance of deployed models, seek issues, and perform regular maintenance to ensure models remain accurate and effective.- Make sure that model deployment and data handling align with security and regulatory requirements.- Implement standard methodologies for data privacy and protection.- Create and maintain documentation for deployment processes, model performance, and system configurations.-

Keyskills :
DevOpsMachine LearningContainerizationOrchestrationCloud ServicesAWSAzureData PrivacyScrumETLCICDGoogle CloudData ProtectionML Ops