Overview
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* This role requires a skilled Data Engineer with expertise in MLOps and machine learning modeling to build robust data pipelines, ML infrastructure, and deployment pipelines for payment processing systems, fraud detection algorithms, and financial analytics solutions.
* You will work on designing, developing, and maintaining scalable data pipelines using Python, Airflow, and PySpark to process large volumes of financial transaction data.
* A strong proficiency in Python programming and data processing frameworks (PySpark) is required, along with experience with workflow orchestration tools, particularly Airflow.
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Key Responsibilities:
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* Design, develop, and maintain scalable data pipelines using Python, Airflow, and PySpark to process large volumes of financial transaction data.
* Implement and optimize MLOps infrastructure on AWS to automate the full machine learning lifecycle from development to production.
* Build and maintain deployment pipelines for ML models using SageMaker and other AWS services.
* Collaborate with data scientists and business stakeholders to implement machine learning solutions for fraud detection, risk assessment, and financial forecasting.
* Ensure data quality, reliability, and security across all data engineering workloads.
* Optimize data architecture to improve performance, scalability, and cost-efficiency.
* Implement monitoring and alerting systems to ensure production ML models perform as expected.
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Requirements and Qualifications:
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* 3-5 years of experience in Data Engineering with a focus on MLOps in production environments.
* Strong proficiency in Python programming and data processing frameworks (PySpark).
* Experience with workflow orchestration tools, particularly Airflow.
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About the Job:
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This role offers the opportunity to work with state-of-the-art machine learning and cloud infrastructure in a fast-paced, growth-oriented environment. The ideal candidate is passionate about innovation and has a strong desire to contribute to the development of cutting-edge fintech solutions.