DevOps Engineer – AI Application Development

Job Description

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 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.
  
  

Our Culture & Commitment to You

At Emerson, we prioritize a workplace where every employee is valued, respected, and empowered to grow. We foster an environment that encourages innovation, collaboration, and diverse perspectives—because we know that great ideas come from great teams. Our commitment to ongoing career development and growing an inclusive culture ensures you have the support to thrive. Whether through mentorship, training, or leadership opportunities, we invest in your success so you can make a lasting impact. We believe diverse teams, working together are key to driving growth and delivering business results.

We recognize the importance of employee wellbeing. We prioritize providing competitive benefits plans, a variety of medical insurance plans, Employee Assistance Program, employee resource groups, recognition, and much more. Our culture offers flexible time off plans, including paid parental leave (maternal and paternal), vacation and holiday leave.