Elanco - Senior MLOps Engineer - Python
3 days ago
Your Role : Sr ML Ops engineer.
The MLOps engineers role is service focused and will create data pipeline and engineering infrastructure to support our enterprise machine learning systems.
This role will collaborate with data scientists and statisticians from various Elanco global business functions to facilitate and lead scientific and/or business knowledge discovery, insights, and forecasting.
The MLOps 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.
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.
- Provide expert data PaaS on Azure storage; big data platform services; server-less architectures; Azure SQL DB; NoSQL databases and secure, automated data pipelines.
What You Need to Succeed (minimum qualifications) :
- Bachelors or masters degree in computer science, Engineering, or related field.
- 57 years of experience in software engineering, data engineering, or ML engineering.
What will give you a competitive edge (preferred qualifications) :
- Strong programming experience in Python.
- Solid understanding of machine learning workflows and MLOps concepts.
- Experience with CI/CD, version control (Git/GitHub), and containerization (Docker, Kubernetes).
- Hands-on experience with Azure cloud services (Data Factory, ADLS, Azure SQL, etc.
- Experience deploying ML models to production environments.
- Familiarity with databases (SQL/NoSQL) and data pipeline design (ETL/ELT).
- Ability to translate business requirements into technical implementations.
- Strong problem-solving and debugging skills.
(ref:hirist.tech)
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