Machine Learning Ops Engineer
Machine Learning Ops Engineer
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Job Description
JobSummary:
As an ML Ops Engineer at Emerson you will be responsible for overseeing the endtoend 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.
InthisRoleYourResponsibilitiesWillBe:
- 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
WhoYouAre:
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 wellinformed decisions. You handle the risk that comes with moving forward when the outcome is not certain.
ForThisRoleYouWillNeed:
- 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.
PreferredQualificationsthatSetYouApart:
- 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 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 perspectivesbecause 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.
Employment Type
Full-Time
