DevOps Engineer - AI Application Development

As a DevOps Engineer at Emerson, you will oversee the end-to-end lifecycle of machine learning models, from deployment to monitoring and maintenance. You will collaborate closely with data scientists, machine learning engineers, and development teams to ensure efficient integration of ML models into production systems, delivering high performance. Your responsibilities will include: - Deploying and managing machine learning models in production environments, focusing on scalability, reliability, and performance. - Designing and implementing CI/CD pipelines for ML models to streamline development and deployment processes. - Developing and maintaining infrastructure for model deployment, including containerization (e.g., Docker), orchestration (e.g., Kubernetes), and cloud services (e.g., AWS, Google Cloud, Azure). - Monitoring model performance, addressing issues, and conducting regular maintenance to uphold accuracy and effectiveness. - Ensuring model deployment and data handling comply with security and regulatory requirements, implementing standard data privacy practices. - Creating and updating documentation for deployment processes, model performance, and system configurations, providing detailed reports to collaborators. - Identifying and implementing enhancements to model performance, deployment processes, and infrastructure efficiency. - Participating in Scrum events such as Sprint Planning, Sprint Review, and Sprint Retrospective. To excel in this role, you should possess: - Bachelor's degree in computer science, Data Science, Statistics, or related field, or equivalent experience. - 7+ years of experience in ML Ops, DevOps, or related roles, with expertise in deploying and managing ML models in production environments. - Proficiency in containerization technologies (e.g., Docker), orchestration platforms (e.g., Kubernetes), and cloud services (Azure, AWS). - Experience with CI/CD tools for automating deployment pipelines and monitoring tools for model performance. - Preferred qualifications include prior experience in the engineering domain, working with Scaled Agile Framework (SAFe) teams, knowledge of data engineering and ETL processes, familiarity with version control systems (e.g., Git), and understanding of machine learning model lifecycle management. Emerson offers competitive compensation and benefits programs, comprehensive medical and insurance coverage, and a commitment to diversity, equity, and inclusion. The company supports foreign nationals through Work Authorization Sponsorship, fosters an inclusive environment for employee development, and prioritizes safety across its global network and facilities. Emerson values diversity, equity, and inclusion to create a culture where every employee's unique experiences and perspectives are respected, fostering innovation and the best solutions for customers. As a DevOps Engineer at Emerson, you will oversee the end-to-end lifecycle of machine learning models, from deployment to monitoring and maintenance. You will collaborate closely with data scientists, machine learning engineers, and development teams to ensure efficient integration of ML models into production systems, delivering high performance. Your responsibilities will include: - Deploying and managing machine learning models in production environments, focusing on scalability, reliability, and performance. - Designing and implementing CI/CD pipelines for ML models to streamline development and deployment processes. - Developing and maintaining infrastructure for model deployment, including containerization (e.g., Docker), orchestration (e.g., Kubernetes), and cloud services (e.g., AWS, Google Cloud, Azure). - Monitoring model performance, addressing issues, and conducting regular maintenance to uphold accuracy and effectiveness. - Ensuring model deployment and data handling comply with security and regulatory requirements, implementing standard data privacy practices. - Creating and updating documentation for deployment processes, model performance, and system configurations, providing detailed reports to collaborators. - Identifying and implementing enhancements to model performance, deployment processes, and infrastructure efficiency. - Participating in Scrum events such as Sprint Planning, Sprint Review, and Sprint Retrospective. To excel in this role, you should possess: - Bachelor's degree in computer science, Data Science, Statistics, or related field, or equivalent experience. - 7+ years of experience in ML Ops, DevOps, or related roles, with expertise in deploying and managing ML models in production environments. - Proficiency in containerization technologies (e.g., Docker), orchestration platforms (e.g., Kubernetes), and cloud services (Azure, AWS). - Experience with CI/CD tools for automating deployment pipelines and monitoring tools for model performance. - Preferred qualifications include prior experience in the engineering domain, working with Scaled Agile Framewor