Overview:

JD

Strong hands-on programming experience in Python, SQL for data processing, model implementation, automation, and production-grade pipeline development. Solid understanding of machine learning concepts, model lifecycle, model artifacts, scoring logic, feature engineering, and model validation. Hands-on experience with OpenShift and Docker or similar enterprise data science platforms. Experience building and managing workflow orchestration using Apache Airflow, including DAG creation, scheduling, dependency management, monitoring, and troubleshooting.

Strong SQL skills and experience working with enterprise data platforms such as Snowflake, Hadoop, Hive, or relational databases. Experience with GitLab, GitHub, or similar version control and CI/CD tools. Ability to understand model development code and convert it into scalable, maintainable, and production-ready implementation pipelines. Experience with batch model scoring, data extraction, feature generation, post-processing, and output delivery processes. Knowledge of testing practices including unit testing, integration testing, regression testing, and production validation.

Strong documentation skills with the ability to create implementation guides, deployment notes, runbooks, and technical specifications. Good understanding of Agile delivery practices and ability to work within Scrum or Kanban execution models. Strong problem-solving, debugging, communication, and stakeholder collaboration skills.

Skills:

ML