DevOps Engineer - AI Application Development

Job Summary:

• 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.

In this Role, 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

Who You Are:

You quickly and critically 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 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 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.

Emersons compensation and benefits programs are designed to be daring 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 crafting a global workplace that supports diversity, equity and embraces inclusion . We encourage foreign nationals to join us through our Work Authorization Sponsorship .

We attract, develop, and retain outstanding 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 crucial 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 strive buildate an organization our people are proud to represent.