ML Ops Engineer

Industry: Technology
Remote
Company size: 251-10K
Full-time
salary
Not disclosed

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Job description

A leading innovative company focused on revolutionizing agriculture by creating intelligent machinery is looking for an ML Ops Engineer. As a member of the engineering team, you will help develop the end-to-end Computer Vision/Machine Learning (CV/ML) pipeline and other infrastructures for the product engineering process. The company has dedicated itself to making a positive impact through technology by creating a space where environment and economic sustainability intersect. The selected candidate will work in a high-impact role focusing on code, versioning of datasets, models, and production endpoints that will enable various teams to collaborate, experiment, and scale fast.

 

Job Responsibilities:

  • Design and architect advanced auto CV/ML pipeline infrastructure
  • Deliver a higher level of usability and reliability to end-user groups
  • Support cloud-based MLOps at scale
  • Work on Continuous Integration/Delivery for the internal user groups’ CV/ML projects
  • Enhance and extend the DataOps and MLOps infrastructure and functional processes
  • Sustain the internal user groups to make adequate use of the CV/ML channels

Job Requirements:

  • Bachelor’s/Master’s degree in Engineering, Computer Science (or equivalent experience)
  • 4+ years of relevant industry experience in MLOps, DevOps, Site Reliability, and other related aspects
  • Strong proficiency with Python
  • Extensive skills with AWS infrastructure or other cloud providers
  • Experience with Jenkins or other CI/CD tools
  • Understand primary ML practice and production workflows
  • Demonstrable skills using Kubeflow/Kubernetes to build large scale production pipelines
  • Sound leadership skills with an ability to train and supervise other ML engineers/data scientists to make efficient/effective use of the pipelines
  • Core competency with infrastructure as code tools such as Terraform and Cloudformation
  • Proficiency with large scale data pipelines is helpful

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