LevelAI Hiring Drive 2024 For MLOps Engineer | Work From Home

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LevelAI Hiring Drive 2024: LevelAI Hiring Drive 2024 for its MLOps Engineer.LevelAI Drive organizes off-campus drives for candidates in any batch. To apply, candidates should have a bachelor’s in Computer Science. if you are interested, please apply as soon as possible.

Company Name: LevelAI

Role: MLOps Engineer

Location: Remote

Experience: 2 years

Qualification: Bachelors in Computer Science 

Batch: Any

Job Type: Full Time

Salary: Best in Industry

LevelAI Hiring Drive 2024

Job Description:

  • As a critical member of the team, your work will be cutting-edge technologies and will play a high-impact role in shaping the future of AI-driven enterprise applications.
  • You will directly work with people who’ve worked at Amazon, Facebook, Google, and other technology companies in the world.
  • With Level AI, you will get to have fun, learn new things, and grow along with us.

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  • Bachelors in Computer Science or related fields. 
  • Minimum 2 years experience in DevOps role combined with developing deployment/serving pipelines and orchestrating the deployment of ML models for production-grade systems.
  • Minimum 1-year experience in Kubernetes, Docker, CI/CD tools, Prometheus, preferably working with ML models.
  • Minimum 1 year of experience with ML Operations tools such as KubeFlow, MLFlow, Metaflow, or Sagemaker.
  • Experience with Triton inference server, SeldonCore, K-Serve and related tool stack is a huge bonus.
  • Demonstrated experience serializing/exporting and serving deep learning models at scale, and deploying using modern ML platforms.
  • Strong troubleshooting skills to keep our ML models highly available.
  • You have a passion for learning and adopting new technologies with a focus on finding the right tool for the job, and a sense of ownership of your work.
  • Self-starter with excellent verbal and written communication and presentation skills.


  • Create and develop ML Operations Pipelines which allow for controlled and continuous enhancement of existing and new ML features and training pipelines.
  • Design, build, and develop state of art machine Learning system infrastructure core components and architect platforms to create, train and deploy ML models.
  • Automate the operational support for model training and model serving pipelines.
  • Build operating dashboards and charts to track system errors, performance and enable root cause analysis.
  • Identify gaps and evaluate relevant tools and technologies as needed to improve processes and systems, leveraging open-source and cloud computing technologies to build effective solutions.
  • Collaborate with the AI team to drive ML projects from conception to completion and production monitoring.

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