DevOps MLops Engineer
CloudRay Inc
Full-time
Indiana
Job description
Budget - 30 LPA
WFO - Pune
Emerson is a global leader in automation technology and software. Through our deep domain expertise and legacy of flawless execution, Emerson helps customers in critical industries like life sciences, energy, power and renewables, chemical and advanced factory automation operate more sustainably while improving productivity, energy security and reliability.
With global operations and a comprehensive portfolio of software and technology, we are helping companies implement digital transformation to measurably improve their operations, conserve valuable resources and enhance their safety.
We offer equitable opportunities, celebrate diversity, and embrace challenges with confidence that, together, we can make an impact across a broad spectrum of countries and industries. Whether you're an established professional looking for a career change, an undergraduate student exploring possibilities, or a recent graduate with an advanced degree, you'll find your chance to make a difference with Emerson. Join our team – let's go!
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 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.
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 decisively 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 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 (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 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.
WFO - Pune
Emerson is a global leader in automation technology and software. Through our deep domain expertise and legacy of flawless execution, Emerson helps customers in critical industries like life sciences, energy, power and renewables, chemical and advanced factory automation operate more sustainably while improving productivity, energy security and reliability.
With global operations and a comprehensive portfolio of software and technology, we are helping companies implement digital transformation to measurably improve their operations, conserve valuable resources and enhance their safety.
We offer equitable opportunities, celebrate diversity, and embrace challenges with confidence that, together, we can make an impact across a broad spectrum of countries and industries. Whether you're an established professional looking for a career change, an undergraduate student exploring possibilities, or a recent graduate with an advanced degree, you'll find your chance to make a difference with Emerson. Join our team – let's go!
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 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.
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 decisively 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 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 (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 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.