ML Ops Developer

As an ML Ops 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 effectively integrated into production systems and deliver high performance.

In this Role, Your Responsibilities Will Be:

• Deploy and manage 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).
• Monitor the performance of deployed models, troubleshoot issues, and perform regular maintenance to ensure models remain accurate and effective.
• Ensure that model deployment and data handling comply with security and regulatory requirements. Implement best practices for data privacy and protection.
• Create and maintain documentation for deployment processes, model performance, and system configurations. Provide clear and comprehensive reports to stakeholders.
• 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

Who You Are:

You quickly and decisively act in constantly evolving, unexpected situations. You adjust communication content and style to meet the needs of diverse partners. You always keep the end in sight; puts in extra effort to meet deadlines. You analyze multiple and diverse sources of information to define problems accurately before moving to solutions. You observe situational and group dynamics and select best-fit approach.

For This Role, You Will Need:

• Bachelor’s degree in computer science, Data Science, Statistics, or a related field or a master's degree or higher is preferred.
• Total 7+ years of industry experience
• More than 3 years of experience in ML Ops, DevOps, or a related role, with a solid understanding of deploying and managing machine learning models in production environments.
• Experience with containerization technologies (e.g., Docker) 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 monitoring and logging tools to track model performance and system health.