About the Role:
We are seeking an experienced Data Engineer with strong MLOps expertise and machine learning modeling experience in the financial domain.
The ideal candidate will be responsible for building robust data pipelines and ML infrastructure to support payment processing systems, fraud detection algorithms, and financial analytics solutions.
Key responsibilities include designing, developing, and maintaining scalable data pipelines using Python, Airflow, and PySpark, implementing and optimizing MLOps infrastructure on AWS, and collaborating with data scientists and business stakeholders to implement machine learning solutions.
Qualifications and skills required include 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 (Airflow), and hands-on experience with AWS stack, especially SageMaker, Lambda, S3, and other relevant services.
Job Responsibilities:
* 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.
Requirements:
* 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.
* 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).
* Familiarity with containerization (Docker) and CI/CD pipelines.
* Excellent problem-solving skills and ability to work in a fast-paced fintech environment.