Job Objective:
Design and deliver scalable real-time data and machine learning solutions by building robust ingestion and transformation frameworks across Hadoop ecosystems. Enable end-to-end ML model operationalization and performance optimization, while supporting multi-modal data processing and development of engineering tools and applications.
Responsibilities:
- Design and develop highly scalable, Real time systems using Hadoop ecosystem components(Iceberg, Spark, Ozone, Trino, Hive, Ranger, Kafka, Flink and Nifi)
- Build robust data ingestion and transformation frameworks using Java, Spark, Python, and shell scripting for ingesting multi model data(image, audio, video, unstructured documents) with both batch and real-time.
- Develop fullstack applications and internal engineering tools using Python, shell scripting, and modern web frameworks (e.g., Flask, React).
- Collaborate closely with data scientists to operationalize machine learning models using Cloudera Machine Learning (CML).
- Perform performance tuning and optimization of data applications on Hadoop to ensure optimal resource utilization.
Requirements:
- Experience working with ML platforms such as CML, Spark MLlib, and Python ML libraries (scikitlearn, XGBoost), including model deployment.
- Design and develop highly scalable, Real time systems using Hadoop ecosystem components(Iceberg, Spark, Ozone, Trino, Hive, Ranger, Kafka, Flink and Nifi)
- Build robust data ingestion and transformation frameworks using Java, Spark, Python, and shell scripting for ingesting multi model data(image, audio, video, unstructured documents) with both batch and real-time.
- Develop fullstack applications and internal engineering tools using Python, shell scripting, and modern web frameworks (e.g., Flask, React).
- Collaborate closely with data scientists to operationalize machine learning models using Cloudera Machine Learning (CML).
- Perform performance tuning and optimization of data applications on Hadoop to ensure optimal resource utilization.