Data Scientist

Role : MLOps Engineer

Location : Remote(INDIA)

Contract : 6-12 Months

Interview : Video

MLOps Engineer

Skillset: Kubernetes, AKS, Azure cloud, Azure DevOps, Databricks

Nice to have: MLOps, ML Infrastructure deployments

Description:

We are looking for an experienced Machine Learning Operations Engineer who has experience working with design, development and implementation of AI/ML applications and managing the lifecycle of Machine Learning models.

The role of an MLOps Engineer is at intersection of Data Scientist, Data Engineer, and DevOps Engineer. You'll be working in a team of engineers that takes on a wide array of responsibilities that encompass building all the infrastructure necessary to take a trained ML Model , integrate and deploy, making it available to other applications.

Job Responsibilities:

• Design and deploy scalable infrastructure for ML workloads using cloud platforms and containerization technologies (e.g., Docker, Kubernetes)
• Work with teams to design and build cloud hosted, automated pipelines that run, monitor, and retrain ML Models for business applications
• Design and implement Model and Pipeline validation procedures alongside teams of Data Scientists, Data Engineers, and other ML Engineers
• Optimize and refactor development code so that it can be moved to production
• Build Data, Feature Engineering Pipelines for new and existing models
• Assemble configurations and specifications to automatically build environments in production
• Create and develop in CI/CD Pipelines which allow for controlled and continuous enhancement of existing work and new features during both development and production phases