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Machine Learning Engineer

External
DIGIWORLD TECHNOLOGIES PTE. LTD. logoDigiworld Technologies · Ubi Techpark, Singapore
S$96K–S$132K/yrContractUnknown3d ago
Information Technology
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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.

Additional Information

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.


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