ML Engineer

Responsibilities: 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 • 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 full‑stack 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. • Experience working with ML platforms such as CML, Spark MLlib, and Python ML libraries (scikit‑learn, XGBoost), including model deployment. Key Skills: • Experience with Python, Java, Scala, or C++ • ML Frameworks & Libraries – XGBoost, Scikit‑learn, Tensor Flow/keras, Hugging face (NLP/NLQ/Gen AI use cases) • Full-Stack Development • Performance Optimization • Data Engineering & Ingestion Frameworks • Collaboration with Data Science Teams