Description

You will build and deploy machine learning models and production pipelines within a cloud environment.

Responsibilities

  • Implement and deploy models into production using MLOps best practices.
  • Collaborate with Data Scientists, Data Engineers, and application engineers to create inferencing pipelines and governance for the ML/DL model lifecycle.
  • Design and implement technical solutions in coordination with dependent teams.
  • Participate in code reviews and contribute to automated test suites to support continuous integration.
  • Build ETL jobs and data pipelines using UC4 or Airflow.

Required Skills

  • 3+ years of experience in machine learning or related fields.
  • Proficiency in Python and libraries including Pandas and NumPy.
  • Experience with cloud platforms (GCP, AWS, or Azure) and scaling containerized applications using Docker and Kubernetes.
  • Hands-on experience with Apache Spark, PySpark, Kafka, and Hadoop.
  • Strong knowledge of SQL and RDBMS databases.
  • Experience with MLFlow and DVC.
  • Familiarity with CI/CD tools such as Jenkins and SonarQube.

Preferred Skills

  • Experience working with big data technologies and large-scale data processing.

Key Skills
Education

Any degree

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