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Job Location: Noida
Roles and Responsibilities
Senior MLops Engineer
A combination of machine learning, data engineering,pyhton and DevOps practices is required in this field.
A quick learner with clear and good communication skills.
Active Directory, DNS, automation tools (Puppet, Chef, Jenkins, Docker).
Desired Candidate Profile
- The demands for good programming knowledge, hands-on experience with ML frameworks, libraries, agile environments and deploying machine learning solutions using DevOps principles is quite high.
- Machine Learning, Devops & Python (Mandatory skills)
- Machine learning is heavily reliant on data, so an experienced MLOps engineer should be well-versed in data structures, data modelling, and database management systems.
- DevOps engineers should always collaborate with Quality Assurance (QA) teams and be aware of the testing history throughout the CI/CD cycle. Understanding how your code is tested and maintained requires an understanding of the framework/environments led by QA.
- Understand the tools in the pipeline that serve different purposes, such as Continuous Integration servers, Configuration management, Deployment automation, Containers, Infrastructure Orchestration, Monitoring and Analytics, Testing and Cloud Quality tools, and network protocols.
- MLOps is based on the existing DevOps discipline. Knowing how to automate the entire DevOps pipeline, including app performance monitoring, infrastructure settings, and configurations, is a requirement.
- Model validation, model training, and other aspects of evaluating an ML system are in addition to traditional code tests like unit and integration testing.
Job Benefits & Perks
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