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