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 include: - Deploying and handling machine learning models in production environments, ensuring they are scalable, reliable, and performant. - Designing and implementing CI/CD (Continuous Integration/Continuous Deployment) pipelines for ML models to streamline development and deployment processes. - Developing and maintaining the infrastructure required for model deployment, including containerization (e.g., Docker), orchestration (e.g., Kubernetes), and cloud services (e.g., AWS, Google Cloud, Azure). - Supervising the performance of deployed models, identifying issues, and performing regular maintenance to ensure models remain accurate and effective. - Ensuring model deployment and data handling align with security and regulatory requirements by implementing standard methodologies for data privacy and protection. - Creating and maintaining documentation for deployment processes, model performance, and system configurations, and delivering clear and detailed reports to collaborators. - Identifying and implementing improvements to model performance, deployment processes, and infrastructure efficiency. - Participating in regular Scrum events such as Sprint Planning, Sprint Review, and Sprint Retrospective. For this role, you will need: - A Bachelor's 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 monitor model performance and system health. Preferred qualifications that set you apart: - Prior experience in the 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, you can expect a workplace where every employee is valued, respected, and empowered to grow. The company fosters an environment that encourages innovation, collaboration, and diverse perspectives. Emerson invests in ongoing career development and growing an inclusive culture to ensure you have the support to thrive. The company also prioritizes employee wellbeing by providing competitive benefits plans, a variety of medical insurance plans, Employee Assistance Program, employee resource groups, recognition, and more. Additionally, Emerson offers flexible time off plans, including paid parental leave (maternal and paternal), vacation, and holiday leave. Emerson is a global leader in automation technology and software, helping customers in critical industries operate more sustainably while improving productivity, energy security, and reliability. The company offers equitable opportunities, celebrates diversity, and embraces challenges with confidence. Emerson believes in making a positive impact through every endeavor and is committed to its people, communities, and the world at large. If you are looking for a chance to make a difference and grow professionally, consider joining the team at Emerson.,
