Description

Responsibilities:

  • Continuous Deployment using GitHub Actions, Flux, Kustomize
  • Design and implement cloud solutions, build MLOps on cloud AWS
  • Data science model containerization, deployment using docker, VLLM, Kubernetes
  • Communicate with a team of data scientists, data engineers and architects, document the processes
  • Develop and deploy scalable tools and services for our clients to handle machine learning training and inference.
  • Knowledge of ML models and LLM

Qualifications:

  • 6+ years of experience in ML Ops with strong knowledge in Kubernetes, Python, MongoDB and AWS.
  • Good understanding of Apache SOLR.
  • Proficient with Linux administration.
  • Knowledge of ML models and LLM.
  • Ability to understand tools used by data scientists and experience with software development and test automation
  • Ability to design and implement cloud solutions and ability to build MLOps pipelines on cloud solutions (AWS)
  • Experience working with cloud computing and database systems
  • Experience building custom integrations between cloud-based systems using APIs
  • Experience developing and maintaining ML systems built with open-source tools
  • Experience with MLOps Frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc., experience with Docker and Kubernetes
  • Experience developing containers and Kubernetes in cloud computing environments
  • Familiarity with one or more data-oriented workflow orchestration frameworks (Kubeflow, Airflow, Argo, etc.)
  • Ability to translate business needs to technical requirements
  • Strong understanding of software testing, benchmarking, and continuous integration
  • Exposure to machine learning methodology and best practices
  • Good communication skills and ability to work in a team

Key Skills
Education

Any Graduate

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