We are seeking an experienced Data Engineer with strong MLOps expertise and machine learning modeling experience in the financial domain.
Key Responsibilities include:
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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.
The ideal candidate should have 3-5 years of experience in Data Engineering with a focus on MLOps in production environments. Proficiency in Python programming and data processing frameworks (PySpark) is also required.
Additionally, experience with workflow orchestration tools, such as Airflow, and hands-on experience with the AWS stack, especially SageMaker, Lambda, S3, and other relevant services, is highly desirable. Knowledge of financial services or payment processing domain is also a plus.