Senior MLOps engineer

At Elanco (NYSE: ELAN) – it all starts with animals!

As a global leader in animal health, we are dedicated to innovation and delivering products and services to prevent and treat disease in farm animals and pets.

We’re driven by our vision of ‘Food and Companionship Enriching Life’ and our approach to sustainability – the Elanco Healthy Purpose™ – to advance the health of animals, people, the planet and our enterprise.

At Elanco, we pride ourselves on fostering a diverse and inclusive work environment.

We believe that diversity is the driving force behind innovation, creativity, and overall business success.

Here, you’ll be part of a company that values and champions new ways of thinking, work with dynamic individuals, and acquire new skills and experiences that will propel your career to new heights.

Making animals’ lives better makes life better – join our team today!

Your Role: Senior ML Ops engineer

The ML Ops Engineer will be responsible for designing, implementing, and maintaining machine learning infrastructure, pipelines, and workflows.

This role will require a deep understanding of data management, software development, and cloud computing.

The successful candidate will work closely with data scientists, software engineers, and other stakeholders to ensure that machine learning models are deployed, monitored, and updated efficiently and effectively.

The ML Ops engineer is expected to work with teams spread globally across different time zones.

Your Responsibilities:


* Deploy and maintain machine learning models, pipelines, and workflows in production environment.


* Re-package (deployment process) ML models that have been developed in the non-production ML environment by ML Teams for deployment to the production ML environment.


* Perform the required MLOps engineering development to refactor the non-production ML model implementation to an "ML as Code" implementation.


* Create, manage, and execute ServiceNow change requests in accordance with the Elanco IT Change Management process to manage the deployment of new models.


* Build and maintain machine learning infrastructure that is scalable, reliable, and efficient.


* Collaborate with data scientists and software engineers to design and implement machine learning workflows.


* Implement monitoring and logging tools to ensure that machine learning models are performing optimally.


* Identify and evaluate new technologies to improve performance, maintainability, and reliability of our machine learning systems.


* Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.


* Support model development, with an emphasis on auditability, versioning, and data security.


* Create and maintain technical documentation for machine learning infrastructure and workflows.


* Stay up to date with the latest developments in machine learning and cloud computing technologies.
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