Backed by Y Combinator.
About the role
As MLOps Engineer, you will work collaboratively with Data Scientists and Data engineers to deploy and operate advanced analytics machine learning models. You’ll help automate and streamline Model development and Model operations. You’ll build and maintain tools for deployment, monitoring, and operations. You’ll also troubleshoot and resolve issues in development, testing, and production environments. • Enable Model tracking, model experimentation, Model automation • Develop scalable ML pipelines
What they're looking for
- • 3-5 years experience building production-quality software
- • Strong experience in System Integration, Application Development or DataWarehouse projects across technologies used in the enterprise space
- • Basic Knowledge of MLOps, machine learning and docker
- • Object-oriented languages (e.g. Python, PySpark, Java, C#, C++ )
- • Experience developing CI/CD components for production ready ML pipeline
- • Database programming using any flavors of SQL
More about this role
It's fun to work in a company where people truly BELIEVE in what they are doing!
We're committed to bringing passion and customer focus to the business.
Bangalore, Mumbai, Pune, Gurgaon, Chennai, Hyderabad, Coimbatore, Noida
Building the machine learning production System(or MLOps) is the biggest challenge most large companies currently have in making the transition to becoming an AI-driven organization. This position is an opportunity for an experienced, server-side developer to build expertise in this exciting new frontier. You will be part of a team deploying state-of-the-art AI solutions for Fractal clients.
As MLOps Engineer, you will work collaboratively with Data Scientists and Data engineers to deploy and operate advanced analytics machine learning models. You’ll help automate and streamline Model development and Model operations. You’ll build and maintain tools for deployment, monitoring, and operations. You’ll also troubleshoot and resolve issues in development, testing, and production environments.
• Enable Model tracking, model experimentation, Model automation • Develop scalable ML pipelines
• Develop MLOps components in Machine learning development life cycle using...
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