MLOps Engineer

Summary

Data has never been more valuable and vulnerable. As cybercriminals become more sophisticated and regulations become stricter, organizations struggle to answer one key question: “Is my data safe?”

At Varonis, we see the world of cybersecurity differently. Instead of chasing threats, we believe that the most practical approach is to protect data from the inside out. We’ve building the industry’s first fully autonomous data security platform to help our customers dramatically reduce risk with minimal human effort.

At Varonis, we move fast. We’re an ultra-collaborative company with brilliant people who care deeply about the details. Together, we’re solving interesting and complex puzzles to keep the world’s data safe.

We work in a flexible, hybrid model, so you can choose the home-office balance that works best for you.

Hands-on key role in our science team. Build, support, drive, collaborate, and guide on our Machine Learning operation pipelines built on Azure.

What You'll Be Doing:

• You will be responsible for defining, implementing, and maintaining a *complete* MLOps pipelines on Azure.
• Collaborate with Data Scientists, Engineering and Product teams to enable efficient and valuable ML-based solutions.
• Analyze data throughout ML pipelines to follow the Scientist's guidance and manage feedback processes.
• Stay updated on the latest ML and MLOps tools and practices to ensure quality and release efficiency.
  
  

• 3+ years of hands-on experience with cloud base infrastructures.
• 3+ years of hands-on experience with data handling, transformation, analysis, and management using ETL/ELT processes.
• 3+ years of experience in training, deploying, maintaining, and monitoring ML models using Cloud native solutions.
• Familiarity with DevOps principles and Agile methodologies.
• Proficient in Python and expertise with Python ML frameworks like scikit-learn, Tensorflow, PyTorch.
• Experience with Jupyter Notebooks.
• Working knowledge of the fundamentals of ML technologies and algorithms.
  
  

• Collaboration skills with the ability to work in a team.
• Proven ability to work independently, communicate effectively, and drive projects with minimal supervision.
• Low-Code to No-Code approach.
  
  

• Expertise in Azure cloud environments, including Kubernetes, compute clusters, ARM templates, Monitoring, Security, virtual networks, and storage solutions.
• Experience in Azure ML practices. including AzureML, RAI, Data Factory.
• A comprehensive knowledge of the ML technologies and algorithms - especially of Classification, Time-Series Forecasts and Regressions modeling.