Machine Learning Ops Engineer

Job Summary:

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, solve issues, and perform regular maintenance to ensure models remain accurate and effective.
• Ensure 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. Provide clear and comprehensive reports to customers.
• 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 promote high visibility of shared contributions to goals. You quickly and significantly take action in constantly evolving, unexpected situations. You actively seek guidance from pertinent sources to make timely and well-informed decisions. You handle the risk that comes with moving forward when the outcome is not certain.

For This Role, You Will Need:

• In computer science, Data Science, Statistics, or a related field or or higher is preferred.
• Total 5 to 6 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 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.

Preferred Qualifications that Set You Apart:

• Prior experience in engineering domain would be nice to have
• Prior experience in working with teams in Scaled Agile Framework (SAFe) is 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.

Our Offer to You:

By joining Emerson, you will be given the opportunity to make a difference through the work you do.

Emerson's compensation and benefits programs are designed to be competitive within the industry and local labor markets . We also offer a comprehensive medical and insurance coverage to meet the needs of our employees.

We are committed to creating a global workplace that supports diversity, equity and embraces inclusion . We welcome foreign nationals to join us through our Work Authorization Sponsorship .

We attract, develop, and retain exceptional people in an inclusive environment, where all employees can reach their greatest potential . We are dedicated to the ongoing development of our employees because we know that it is critical to our success as a global company.

We have established our Remote Work Policy for eligible roles to promote Work-Life Balance through a hybrid work set up where our team members can take advantage of working both from home and at the office.

Safety is paramount to us, and we are relentless in our pursuit to provide a Safe Working Environment across our global network and facilities.

Through our benefits, development opportunities, and an inclusive and safe work environment, we aim to create an organization our people are proud to represent.