As a Data Engineer with MLOps expertise, you will be responsible for building robust data pipelines and ML infrastructure to support payment processing systems, fraud detection algorithms, and financial analytics solutions.
The ideal candidate will have 3-5 years of experience in Data Engineering with a focus on MLOps in production environments. You will work closely with data scientists and business stakeholders to implement machine learning solutions for fraud detection, risk assessment, and financial forecasting.
Key Responsibilities
* Data Pipeline Development: Design, develop, and maintain scalable data pipelines using Python, Airflow, and PySpark to process large volumes of financial transaction data.
* MLOps Infrastructure: Implement and optimize MLOps infrastructure on AWS to automate the full machine learning lifecycle from development to production.
* ML Model Deployment: Build and maintain deployment pipelines for ML models using SageMaker and other AWS services.
* Collaboration: Work with data scientists and business stakeholders to implement machine learning solutions for fraud detection, risk assessment, and financial forecasting.
* Data Quality and Security: Ensure data quality, reliability, and security across all data engineering workloads.
Requirements
* Strong proficiency in Python programming and data processing frameworks (PySpark).
* Experience with workflow orchestration tools, particularly Airflow.
* Hands-on experience with AWS stack, especially SageMaker, Lambda, S3, and other relevant services.
* Working knowledge of machine learning model deployment and monitoring in production.
* Experience with data modeling and database systems (SQL and NoSQL).
* Knowledge of financial services or payment processing domain is highly desirable.